diff --git a/5-Clustering/2-K-Means/notebook.ipynb b/5-Clustering/2-K-Means/notebook.ipynb
index b25d4f188..f7d541b67 100644
--- a/5-Clustering/2-K-Means/notebook.ipynb
+++ b/5-Clustering/2-K-Means/notebook.ipynb
@@ -1,84 +1,172 @@
 {
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
-   "display_name": "Python 3.7.0 64-bit ('3.7')"
-  },
-  "metadata": {
-   "interpreter": {
-    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
  "cells": [
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "# Nigerian Music scraped from Spotify - an analysis"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
-      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
-      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
-      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
-      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
-      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
-      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
-      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
-      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
-      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "pip install seaborn"
+    "# %pip install seaborn"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "Start where we finished in the last lesson, with data imported and filtered."
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "0                    Sparky            Mandy & The Jungle   \n",
@@ -107,11 +195,11 @@
        "2       0.0424  130.005               4  \n",
        "3       0.1130  111.087               4  \n",
        "4       0.0447  105.115               4  "
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      ]
      },
+     "execution_count": 1,
      "metadata": {},
-     "execution_count": 6
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -126,37 +214,36 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "We will focus only on 3 genres. Maybe we can get 3 clusters built!"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0.5, 1.0, 'Top genres')"
       ]
      },
+     "execution_count": 2,
      "metadata": {},
-     "execution_count": 7
+     "output_type": "execute_result"
     },
     {
-     "output_type": "display_data",
      "data": {
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\n"
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",
+      "text/plain": [
+       "<Figure size 1000x700 with 1 Axes>"
+      ]
      },
-     "metadata": {
-      "needs_background": "light"
-     }
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -171,12 +258,148 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "output_type": "execute_result",
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Kasala</td>\n",
+       "      <td>Pioneers</td>\n",
+       "      <td>DRB Lasgidi</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>184800</td>\n",
+       "      <td>26</td>\n",
+       "      <td>0.803</td>\n",
+       "      <td>0.1270</td>\n",
+       "      <td>0.525</td>\n",
+       "      <td>0.000007</td>\n",
+       "      <td>0.1290</td>\n",
+       "      <td>-10.034</td>\n",
+       "      <td>0.1970</td>\n",
+       "      <td>100.103</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Pull Up</td>\n",
+       "      <td>Everything Pretty</td>\n",
+       "      <td>prettyboydo</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>202648</td>\n",
+       "      <td>29</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.4520</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>0.004490</td>\n",
+       "      <td>0.5900</td>\n",
+       "      <td>-9.840</td>\n",
+       "      <td>0.1990</td>\n",
+       "      <td>95.842</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
@@ -205,16 +428,277 @@
        "4       0.0447  105.115               4  \n",
        "5       0.1970  100.103               4  \n",
        "6       0.1990   95.842               4  "
-      ],
-      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      ]
      },
+     "execution_count": 3,
      "metadata": {},
-     "execution_count": 8
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "df.head()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='release_date'>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 4000x4000 with 12 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpreprocessing\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m LabelEncoder  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      2\u001b[39m le = LabelEncoder()\n\u001b[32m      3\u001b[39m x = df.loc[:, (\u001b[33m'\u001b[39m\u001b[33martist_top_genre\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mpopularity\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mdanceability\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33macousticness\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33mloudness\u001b[39m\u001b[33m'\u001b[39m,\u001b[33m'\u001b[39m\u001b[33menergy\u001b[39m\u001b[33m'\u001b[39m)]\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder  # pyright: ignore[reportMissingImports]\n",
+    "le = LabelEncoder()\n",
+    "x = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "y = df['artist_top_genre']\n",
+    "x['artist_top_genre'] = le.fit_transform(x['artist_top_genre'])  # pyright: ignore[reportUndefinedVariable]\n",
+    "y = le.transform(y)  # pyright: ignore[reportUndefinedVariable]\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split  # pyright: ignore[reportMissingImports]\n",
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier  # pyright: ignore[reportMissingImports]\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=42)  # pyright: ignore[reportUndefinedVariable]\n",
+    "model.fit(x_train, y_train)\n",
+    "\n",
+    "y_pred = model.predict(x_test)\n",
+    "\n",
+    "from sklearn.metrics import classification_report, confusion_matrix  # pyright: ignore[reportMissingImports]\n",
+    "print(confusion_matrix(y_test, y_pred))\n",
+    "print(classification_report(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcluster\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KMeans  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      3\u001b[39m nclusters = \u001b[32m3\u001b[39m \n\u001b[32m      4\u001b[39m seed = \u001b[32m0\u001b[39m\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans  # pyright: ignore[reportMissingImports]\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "X = x.values  # pyright: ignore[reportUndefinedVariable]\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)  # pyright: ignore[reportUndefinedVariable]\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)  # pyright: ignore[reportUndefinedVariable]\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m metrics  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      2\u001b[39m score = metrics.silhouette_score(X, y_cluster_kmeans)\n\u001b[32m      3\u001b[39m score\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn import metrics  # pyright: ignore[reportMissingImports]\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'sklearn'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01msklearn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcluster\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m KMeans  \u001b[38;5;66;03m# pyright: ignore[reportMissingImports]\u001b[39;00m\n\u001b[32m      2\u001b[39m wcss = []\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m1\u001b[39m, \u001b[32m11\u001b[39m):\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'sklearn'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans  # pyright: ignore[reportMissingImports]\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(x=range(1, 11), y=wcss, marker='o', color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans  # pyright: ignore[reportMissingImports]\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/6-NLP/2-Tasks/solution/assignment.py b/6-NLP/2-Tasks/solution/assignment.py
new file mode 100644
index 000000000..266a82a74
--- /dev/null
+++ b/6-NLP/2-Tasks/solution/assignment.py
@@ -0,0 +1,40 @@
+from textblob import TextBlob
+from textblob.np_extractors import ConllExtractor
+extracctor = ConllExtractor();
+
+
+def mani():
+    print("Hello, I am Paskal, the friendly robot")
+    print("You can end the conversation at any time typing  'bye'")
+    print("After typing each sentence presthe Enter for the sentiment analysis")
+    print("How are you feeling today?")
+
+    while True:
+        user_input = input("You:")
+        if user_input.lower() in ('bye', "exit", "quit"):
+            break
+
+        user_blob = TextBlob(user_input, np_extractor=extracctor)
+        polarity = user_blob.polarity
+        noun_phrases = user_blob.noun_phrases
+
+        if polarity <= -0.5:
+            response = "Oh Dear, that sounds really bad!"
+        elif polarity <= 0:
+            response = "Hmm, that's not great."
+        elif polarity < 0.5:
+            response = "Thats sound positive! "
+        else:
+            response = "Yay! That sounds awesome!"
+        if noun_phrases:
+            np = noun_phrases[0]
+            try:
+                plural_np = np.pluralize()
+            except Exception:
+                plural_np = np
+            response += f"Can you tell me more about {plural_np}?"
+        else:
+            response += "Can you tell me more about that?"
+        print(response)
+    print("Goodbye! It was nice talking to you.")
+mani()