From 7609666fb2e52a1528832b3188ecb9d035f081ad Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Mon, 7 Jun 2021 18:11:05 -0400
Subject: [PATCH] classification 1

---
 1-Introduction/1-intro-to-ML/README.md        |    2 +
 4-Classification/1-Introduction/README.md     |  195 ++-
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 .../solution/data-prep-visual.ipynb           | 1521 -----------------
 .../solution/intro-classification.ipynb       |  563 ------
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 .../notebook.ipynb}                           |    0
 .../2-Classifiers-1/solution/notebook.ipynb   |  336 ++++
 .../translations/README.es.md                 |    0
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 .../3-Classifiers-2/notebook.ipynb            |    0
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 rename 4-Classification/{2-Discriminative => 2-Classifiers-1}/README.md (100%)
 rename 4-Classification/{2-Discriminative => 2-Classifiers-1}/assignment.md (100%)
 rename 4-Classification/{2-Discriminative/translations/README.es.md => 2-Classifiers-1/notebook.ipynb} (100%)
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 rename 4-Classification/{3-Generative => 2-Classifiers-1}/translations/README.es.md (100%)
 rename 4-Classification/{3-Generative => 3-Classifiers-2}/README.md (100%)
 rename 4-Classification/{3-Generative => 3-Classifiers-2}/assignment.md (100%)
 create mode 100644 4-Classification/3-Classifiers-2/notebook.ipynb
 create mode 100644 4-Classification/3-Classifiers-2/solution/notebook.ipynb
 create mode 100644 4-Classification/3-Classifiers-2/translations/README.es.md
 create mode 100644 4-Classification/4-Applied/notebook.ipynb
 create mode 100644 4-Classification/4-Applied/solution/notebook.ipynb

diff --git a/1-Introduction/1-intro-to-ML/README.md b/1-Introduction/1-intro-to-ML/README.md
index 8e431fdbb..1d7a03697 100644
--- a/1-Introduction/1-intro-to-ML/README.md
+++ b/1-Introduction/1-intro-to-ML/README.md
@@ -2,6 +2,8 @@
 
 [![ML, AI, Deep Learning - What's the difference?](https://img.youtube.com/vi/lTd9RSxS9ZE/0.jpg)](https://youtu.be/lTd9RSxS9ZE "ML, AI, Deep Learning - What's the difference?")
 
+python path: https://docs.microsoft.com/en-us/learn/paths/python-language/
+
 > 🎥 Click the image above for a video discussing the difference between Machine Learning, AI, and Deep Learning.
 ## [Pre-lecture quiz](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/1/)
 ### Introduction
diff --git a/4-Classification/1-Introduction/README.md b/4-Classification/1-Introduction/README.md
index bc0c7aa4a..446fecef4 100644
--- a/4-Classification/1-Introduction/README.md
+++ b/4-Classification/1-Introduction/README.md
@@ -1,32 +1,205 @@
 # Introduction to Classification
 
-In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - Classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia. Hope you're hungry!
+In these four lessons, you will discover the 'meat and potatoes' of classic machine learning - Classification. No pun intended - we will walk through using various classification algorithms with a dataset all about the brilliant cuisines of Asia and India. Hope you're hungry!
 
 Classification is a form of [supervised learning](https://wikipedia.org/wiki/Supervised_learning) that bears a lot in common with Regression techniques. If machine learning is all about assigning names to things via datasets, then classification generally falls into two groups: binary classification and multiclass classfication.
 
-Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
-
-Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
-
 [![Introduction to Classification](https://img.youtube.com/vi/eg8DJYwdMyg/0.jpg)](https://youtu.be/eg8DJYwdMyg "Introduction to Classification")
 
 > 🎥 Click the image above for a video: MIT's John Guttag introduces Classification
 
+Remember, Linear Regression helped you predict relationships between variables and make accurate predictions on where a new datapoint would fall in relationship to that line. So, you could predict what price a pumpkin would be in September vs. December, for example. Logistic Regression helped you discover binary categories: at this price point, is this pumpkin orange or not-orange?
+
+Classification uses various algorithms to determine other ways of determining a data point's label or class. Let's work with this recipe data to see whether, by observing a group of ingredients, we can determine its cuisine of origin.
 ## [Pre-lecture quiz](link-to-quiz-app)
 ### Introduction
 
-Before working to clean the data and prepare it for analysis, it's useful to understand several of the algorithms that you will use. 
+Classification is one of the fundamental activities of the machine learning researcher and data scientist. From basic classification of a binary value ("is this email spam or not?") to complex image classification and segmentation using computer vision, it's always useful to be able to sort data into classes and ask questions of it. Or, to state the process in a more scientific way, your classification method creates a predictive model that enables you to map the relationship between input variables to output variables. 
+
+Before starting the process of cleaning our data, visualizing it, and prepping it for our ML tasks, let's learn a bit about the various ways machine learning can be leveraged to classify data.
+
+Derived from [statistics](https://wikipedia.org/wiki/Statistical_classification), classification using classic machine learning uses features, such as 'smoker','weight', and 'age' to determine 'likelihood of developing X disease'. As a supervised learning technique similar to the Regression exercises you performed earlier, your data is labeled and the ML algorithms use those labels to classify and predict classes (or 'features') of a dataset and assign them to a group or outcome.
+
+✅ Take a moment to imagine a dataset about recipes. What would a multiclass model be able to answer? What would a binary model be able to answer? What if you wanted to determine whether a given cuisine was likely to contain Fenugreek? What if you wanted to see if, given a present of a grocery bag full of star anise, artichokes, cauliflower, and horseradish, you could create a typical Indian dish?
+
+## Hello 'classifier'
+
+The question we want to ask of this recipe dataset is actually a **multiclass question**, as we have several potential national cuisines to work with. Given a batch of ingredients, which of these many classes will the data fit?
+
+Scikit-Learn offers several different algorithms to use to classify data, depending on the kind of problem you want to solve. In the next two lessons, you'll learn about several of these algorithms.
+
+## Clean and Balance Your Data
+
+The first task at hand before starting this project is to clean and **balance** your data to get better results. Start with the blank `notebook.ipynb` file ini the root of this folder.
+
+The first think to install is [imblearn](https://imbalanced-learn.org/stable/). This is a Scikit-Learn package that will allow you to better balance the data (you will learn more about this task in a minute).
+
+```python
+pip install imblearn
+```
+
+Then, import the packages you need to import your data and visualize it. Import SMOTE from imblearn.
+
+```python
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+import numpy as np
+from imblearn.over_sampling import SMOTE
+```
+The next task will be to import the data:
+
+```python
+df  = pd.read_csv('../data/recipes.csv')
+```
+
+Check the data's shape:
+
+```python
+df.head()
+```
 
-- Support-vector machines
-- Naive Bayes
-- Decision trees
-- K-nearest neighbor algorithm
+The first five rows look like this:
 
 
+|     | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini |
+| --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- |
+| 0   | 65         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 1   | 66         | indian  | 1      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 2   | 67         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 3   | 68         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 0      | 0        |
+| 4   | 69         | indian  | 0      | 0        | 0     | 0          | 0     | 0            | 0       | 0        | ... | 0       | 0           | 0          | 0                       | 0    | 0    | 0   | 0     | 1      | 0        |
 
-✅ Knowledge Check - use this moment to stretch students' knowledge with open questions
+Get info about this data:
 
+```python
+df.info()
+```
 
+```
+<class 'pandas.core.frame.DataFrame'>
+RangeIndex: 2448 entries, 0 to 2447
+Columns: 385 entries, Unnamed: 0 to zucchini
+dtypes: int64(384), object(1)
+memory usage: 7.2+ MB
+```
+## Learning about cuisines
+
+Now the work starts to become more interesting. Let's discover the distribution of data, per cuisine:
+
+```python
+df.cuisine.value_counts().plot.barh()
+```
+
+![cuisine data distribution](images/cuisine-dist.png)
+
+There are a finite number of cuisines, but the distribution of data is uneven. You can fix that! Before doing so, explore a little more. How much data exactly is available per cuisine?
+
+```python
+thai_df = df[(df.cuisine == "thai")]
+japanese_df = df[(df.cuisine == "japanese")]
+chinese_df = df[(df.cuisine == "chinese")]
+indian_df = df[(df.cuisine == "indian")]
+korean_df = df[(df.cuisine == "korean")]
+
+print(f'thai df: {thai_df.shape}')
+print(f'japanese df: {japanese_df.shape}')
+print(f'chinese df: {chinese_df.shape}')
+print(f'indian df: {indian_df.shape}')
+print(f'korean df: {korean_df.shape}')
+```
+thai df: (289, 385)
+japanese df: (320, 385)
+chinese df: (442, 385)
+indian df: (598, 385)
+korean df: (799, 385)
+
+## Discovering ingredients
+
+Now you can dig deeper into the data and learn what are the typical ingredients per cuisine. You should clean out recurrent data that creates confusion between cuisines, so let's learn about this problem.
+
+Create a function in Python to create an ingredient dataframe. This function will start by dropping an unhelpful column and sort through ingredients by their count:
+
+```python
+def create_ingredient_df(df):
+    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')
+    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]
+    ingredient_df = ingredient_df.sort_values(by='value', ascending=False
+    inplace=False)
+    return ingredient_df
+```
+Now you can use that function to get an idea of top ten most popular ingredients by cuisine:
+
+```python
+thai_ingredient_df = create_ingredient_df(thai_df)
+thai_ingredient_df.head(10).plot.barh()
+```
+![thai](images/thai.png)
+
+```python
+japanese_ingredient_df = create_ingredient_df(japanese_df)
+japanese_ingredient_df.head(10).plot.barh()
+```
+![japanese](images/japanese.png)
+
+```python
+chinese_ingredient_df = create_ingredient_df(chinese_df)
+chinese_ingredient_df.head(10).plot.barh()
+```
+![chinese](images/chinese.png)
+
+```python
+indian_ingredient_df = create_ingredient_df(indian_df)
+indian_ingredient_df.head(10).plot.barh()
+```
+![indian](images/indian.png)
+
+```python
+korean_ingredient_df = create_ingredient_df(korean_df)
+korean_ingredient_df.head(10).plot.barh()
+```
+![korean](images/korean.png)
+
+Now, drop the most common ingredients that create confusion between distinct cuisines. Everyone loves rice, garlic and ginger!
+
+```python
+feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)
+labels_df = df.cuisine #.unique()
+feature_df.head()
+```
+## Balance the dataset
+
+Now that you have cleaned the data, use [SMOTE](https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html) - "Synthetic Minority Over-sampling Technique" - to balance it. This strategy generates new samples by interpolation.
+
+```python
+oversample = SMOTE()
+transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
+```
+By balancing your data, you'll have better results when classifying it. Now you can check the numbers of labels per ingredient:
+
+```python
+print(f'new label count: {transformed_label_df.value_counts()}')
+print(f'old label count: {df.cuisine.value_counts()}')
+```
+new label count: korean      799
+chinese     799
+indian      799
+japanese    799
+thai        799
+Name: cuisine, dtype: int64
+old label count: korean      799
+indian      598
+chinese     442
+japanese    320
+thai        289
+Name: cuisine, dtype: int64
+
+The data is nice and clean, balanced, and very delicious! You can take one more look at the data using `transformed_df.head()` and `transformed_df.info()`. Save a copy of this data for use in future lessons:
+
+```python
+transformed_df.to_csv("../../data/cleaned_cuisine.csv")
+```
+This fresh CSV can now be found in the root data folder.
 ## 🚀Challenge
 
 ## [Post-lecture quiz](link-to-quiz-app)
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diff --git a/4-Classification/1-Introduction/notebook.ipynb b/4-Classification/1-Introduction/notebook.ipynb
index e69de29bb..8fe41283d 100644
--- a/4-Classification/1-Introduction/notebook.ipynb
+++ b/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,28 @@
+{
+ "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
+  },
+  "orig_nbformat": 2
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Delicious Asian Recipes "
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/4-Classification/1-Introduction/solution/data-prep-visual.ipynb b/4-Classification/1-Introduction/solution/data-prep-visual.ipynb
deleted file mode 100644
index a62ec2542..000000000
--- a/4-Classification/1-Introduction/solution/data-prep-visual.ipynb
+++ /dev/null
@@ -1,1521 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\r\n",
-    "import pandas as pd\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "import matplotlib as mpl\r\n",
-    "import numpy as np\r\n",
-    "from imblearn.over_sampling import SMOTE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df  = pd.read_csv('.data/asian_indian_recipes.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>Unnamed: 0</th>\n",
-       "      <th>cuisine</th>\n",
-       "      <th>almond</th>\n",
-       "      <th>angelica</th>\n",
-       "      <th>anise</th>\n",
-       "      <th>anise_seed</th>\n",
-       "      <th>apple</th>\n",
-       "      <th>apple_brandy</th>\n",
-       "      <th>apricot</th>\n",
-       "      <th>armagnac</th>\n",
-       "      <th>...</th>\n",
-       "      <th>whiskey</th>\n",
-       "      <th>white_bread</th>\n",
-       "      <th>white_wine</th>\n",
-       "      <th>whole_grain_wheat_flour</th>\n",
-       "      <th>wine</th>\n",
-       "      <th>wood</th>\n",
-       "      <th>yam</th>\n",
-       "      <th>yeast</th>\n",
-       "      <th>yogurt</th>\n",
-       "      <th>zucchini</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>65</td>\n",
-       "      <td>indian</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>66</td>\n",
-       "      <td>indian</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>67</td>\n",
-       "      <td>indian</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>68</td>\n",
-       "      <td>indian</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>69</td>\n",
-       "      <td>indian</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 385 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
-       "0          65  indian       0         0      0           0      0   \n",
-       "1          66  indian       1         0      0           0      0   \n",
-       "2          67  indian       0         0      0           0      0   \n",
-       "3          68  indian       0         0      0           0      0   \n",
-       "4          69  indian       0         0      0           0      0   \n",
-       "\n",
-       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
-       "0             0        0         0  ...        0            0           0   \n",
-       "1             0        0         0  ...        0            0           0   \n",
-       "2             0        0         0  ...        0            0           0   \n",
-       "3             0        0         0  ...        0            0           0   \n",
-       "4             0        0         0  ...        0            0           0   \n",
-       "\n",
-       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
-       "0                        0     0     0    0      0       0         0  \n",
-       "1                        0     0     0    0      0       0         0  \n",
-       "2                        0     0     0    0      0       0         0  \n",
-       "3                        0     0     0    0      0       0         0  \n",
-       "4                        0     0     0    0      0       1         0  \n",
-       "\n",
-       "[5 rows x 385 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 2448 entries, 0 to 2447\n",
-      "Columns: 385 entries, Unnamed: 0 to zucchini\n",
-      "dtypes: int64(384), object(1)\n",
-      "memory usage: 7.2+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "df.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "korean      799\n",
-       "indian      598\n",
-       "chinese     442\n",
-       "japanese    320\n",
-       "thai        289\n",
-       "Name: cuisine, dtype: int64"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.cuisine.value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#df.keys().values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#display classes in bar graph\r\n",
-    "df.cuisine.value_counts().plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "thai df: (289, 385)\n",
-      "japanese df: (320, 385)\n",
-      "chinese df: (442, 385)\n",
-      "indian df: (598, 385)\n",
-      "korean df: (799, 385)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# ingrediant counts by class count\r\n",
-    "# filter to thai food, display ingredients graph\r\n",
-    "\r\n",
-    "thai_df = df[(df.cuisine == \"thai\")]\r\n",
-    "japanese_df = df[(df.cuisine == \"japanese\")]\r\n",
-    "chinese_df = df[(df.cuisine == \"chinese\")]\r\n",
-    "indian_df = df[(df.cuisine == \"indian\")]\r\n",
-    "korean_df = df[(df.cuisine == \"korean\")]\r\n",
-    "\r\n",
-    "print(f'thai df: {thai_df.shape}')\r\n",
-    "print(f'japanese df: {japanese_df.shape}')\r\n",
-    "print(f'chinese df: {chinese_df.shape}')\r\n",
-    "print(f'indian df: {indian_df.shape}')\r\n",
-    "print(f'korean df: {korean_df.shape}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## What are the top ingredients by class"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_ingredient_df(df):\r\n",
-    "    #transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\r\n",
-    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\r\n",
-    "    # drop ingredients that have a 0 sum\r\n",
-    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\r\n",
-    "    # sort df\r\n",
-    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\r\n",
-    "    return ingredient_df\r\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
-    "thai_ingredient_df.head(10).plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
-    "japanese_ingredient_df.head(10).plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
-    "chinese_ingredient_df.head(10).plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
-    "indian_ingredient_df.head(10).plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
-    "korean_ingredient_df.head(10).plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# TODO add categorical labels to food items - calculated columns to improve accuracy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>almond</th>\n",
-       "      <th>angelica</th>\n",
-       "      <th>anise</th>\n",
-       "      <th>anise_seed</th>\n",
-       "      <th>apple</th>\n",
-       "      <th>apple_brandy</th>\n",
-       "      <th>apricot</th>\n",
-       "      <th>armagnac</th>\n",
-       "      <th>artemisia</th>\n",
-       "      <th>artichoke</th>\n",
-       "      <th>...</th>\n",
-       "      <th>whiskey</th>\n",
-       "      <th>white_bread</th>\n",
-       "      <th>white_wine</th>\n",
-       "      <th>whole_grain_wheat_flour</th>\n",
-       "      <th>wine</th>\n",
-       "      <th>wood</th>\n",
-       "      <th>yam</th>\n",
-       "      <th>yeast</th>\n",
-       "      <th>yogurt</th>\n",
-       "      <th>zucchini</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 380 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
-       "0       0         0      0           0      0             0        0   \n",
-       "1       1         0      0           0      0             0        0   \n",
-       "2       0         0      0           0      0             0        0   \n",
-       "3       0         0      0           0      0             0        0   \n",
-       "4       0         0      0           0      0             0        0   \n",
-       "\n",
-       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
-       "0         0          0          0  ...        0            0           0   \n",
-       "1         0          0          0  ...        0            0           0   \n",
-       "2         0          0          0  ...        0            0           0   \n",
-       "3         0          0          0  ...        0            0           0   \n",
-       "4         0          0          0  ...        0            0           0   \n",
-       "\n",
-       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
-       "0                        0     0     0    0      0       0         0  \n",
-       "1                        0     0     0    0      0       0         0  \n",
-       "2                        0     0     0    0      0       0         0  \n",
-       "3                        0     0     0    0      0       0         0  \n",
-       "4                        0     0     0    0      0       1         0  \n",
-       "\n",
-       "[5 rows x 380 columns]"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# set x and y features\r\n",
-    "# dropping common ingredients to improve accuracy\r\n",
-    "#feature_df= df.drop(['cuisine','Unnamed: 0'], axis=1)\r\n",
-    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\r\n",
-    "labels_df = df.cuisine #.unique()\r\n",
-    "feature_df.head()\r\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# balance data with SMOTE oversamplling to the highest class. Read more here: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\r\n",
-    "oversample = SMOTE()\r\n",
-    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>almond</th>\n",
-       "      <th>angelica</th>\n",
-       "      <th>anise</th>\n",
-       "      <th>anise_seed</th>\n",
-       "      <th>apple</th>\n",
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-       "      <th>armagnac</th>\n",
-       "      <th>artemisia</th>\n",
-       "      <th>artichoke</th>\n",
-       "      <th>...</th>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 380 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
-       "0       0         0      0           0      0             0        0   \n",
-       "1       1         0      0           0      0             0        0   \n",
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-       "3       0         0      0           0      0             0        0   \n",
-       "4       0         0      0           0      0             0        0   \n",
-       "\n",
-       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
-       "0         0          0          0  ...        0            0           0   \n",
-       "1         0          0          0  ...        0            0           0   \n",
-       "2         0          0          0  ...        0            0           0   \n",
-       "3         0          0          0  ...        0            0           0   \n",
-       "4         0          0          0  ...        0            0           0   \n",
-       "\n",
-       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
-       "0                        0     0     0    0      0       0         0  \n",
-       "1                        0     0     0    0      0       0         0  \n",
-       "2                        0     0     0    0      0       0         0  \n",
-       "3                        0     0     0    0      0       0         0  \n",
-       "4                        0     0     0    0      0       1         0  \n",
-       "\n",
-       "[5 rows x 380 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformed_feature_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "new label count: chinese     799\n",
-      "korean      799\n",
-      "indian      799\n",
-      "thai        799\n",
-      "japanese    799\n",
-      "Name: cuisine, dtype: int64\n",
-      "old label count: korean      799\n",
-      "indian      598\n",
-      "chinese     442\n",
-      "japanese    320\n",
-      "thai        289\n",
-      "Name: cuisine, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
-    "print(f'old label count: {df.cuisine.value_counts()}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>cuisine</th>\n",
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-       "      <th>artemisia</th>\n",
-       "      <th>...</th>\n",
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-       "      <th>white_bread</th>\n",
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-       "      <th>whole_grain_wheat_flour</th>\n",
-       "      <th>wine</th>\n",
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-       "    <tr>\n",
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-       "      <td>indian</td>\n",
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-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>indian</td>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 381 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
-       "0  indian       0         0      0           0      0             0        0   \n",
-       "1  indian       1         0      0           0      0             0        0   \n",
-       "2  indian       0         0      0           0      0             0        0   \n",
-       "3  indian       0         0      0           0      0             0        0   \n",
-       "4  indian       0         0      0           0      0             0        0   \n",
-       "\n",
-       "   armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
-       "0         0          0  ...        0            0           0   \n",
-       "1         0          0  ...        0            0           0   \n",
-       "2         0          0  ...        0            0           0   \n",
-       "3         0          0  ...        0            0           0   \n",
-       "4         0          0  ...        0            0           0   \n",
-       "\n",
-       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
-       "0                        0     0     0    0      0       0         0  \n",
-       "1                        0     0     0    0      0       0         0  \n",
-       "2                        0     0     0    0      0       0         0  \n",
-       "3                        0     0     0    0      0       0         0  \n",
-       "4                        0     0     0    0      0       1         0  \n",
-       "\n",
-       "[5 rows x 381 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# export transformed data to new csv for classification\r\n",
-    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\r\n",
-    "transformed_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transformed_feature_df.to_csv(\".data/features_dataset.csv\")\r\n",
-    "transformed_label_df.to_csv(\".data/labels_dataset.csv\")\r\n",
-    "transformed_df.to_csv(\".data/processed.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Build classification model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\r\n",
-    "from sklearn.model_selection import train_test_split, cross_val_score\r\n",
-    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\r\n",
-    "from sklearn.svm import SVC"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(transformed_feature_df, transformed_label_df, test_size=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy is 0.7973311092577148\n"
-     ]
-    }
-   ],
-   "source": [
-    "lr = LogisticRegression(multi_class='ovr',solver='lbfgs')\r\n",
-    "model = lr.fit(X_train, y_train)\r\n",
-    "\r\n",
-    "accuracy = model.score(X_test, y_test)\r\n",
-    "print (\"Accuracy is {}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ingredients: Index(['bean', 'carrot', 'cayenne', 'pea', 'sake', 'scallion', 'sesame_oil',\n",
-      "       'shrimp', 'starch', 'vegetable_oil', 'vinegar'],\n",
-      "      dtype='object')\n",
-      "cusine: chinese\n"
-     ]
-    }
-   ],
-   "source": [
-    "# test an item\r\n",
-    "print(f'ingredients: {X_test.iloc[20][X_test.iloc[20]!=0].keys()}')\r\n",
-    "print(f'cusine: {y_test.iloc[20]}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>chinese</th>\n",
-       "      <td>0.848156</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>japanese</th>\n",
-       "      <td>0.110072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>korean</th>\n",
-       "      <td>0.033688</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>thai</th>\n",
-       "      <td>0.005013</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>indian</th>\n",
-       "      <td>0.003072</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 0\n",
-       "chinese   0.848156\n",
-       "japanese  0.110072\n",
-       "korean    0.033688\n",
-       "thai      0.005013\n",
-       "indian    0.003072"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#rehsape to 2d array and transpose\r\n",
-    "test= X_test.iloc[20].values.reshape(-1, 1).T\r\n",
-    "# predict with score\r\n",
-    "proba = model.predict_proba(test)\r\n",
-    "classes = model.classes_\r\n",
-    "# create df with classes and scores\r\n",
-    "resultdf = pd.DataFrame(data=proba, columns=classes)\r\n",
-    "\r\n",
-    "# create df to show results\r\n",
-    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\r\n",
-    "topPrediction.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred = model.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "     chinese       0.78      0.75      0.76       243\n",
-      "      indian       0.91      0.92      0.91       233\n",
-      "    japanese       0.67      0.77      0.71       244\n",
-      "      korean       0.84      0.78      0.81       241\n",
-      "        thai       0.82      0.77      0.80       238\n",
-      "\n",
-      "    accuracy                           0.80      1199\n",
-      "   macro avg       0.80      0.80      0.80      1199\n",
-      "weighted avg       0.80      0.80      0.80      1199\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(classification_report(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Try different classifiers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\r\n",
-    "C = 10\r\n",
-    "# Create different classifiers.\r\n",
-    "classifiers = {\r\n",
-    "    'L1 logistic': LogisticRegression(C=C, penalty='l1',\r\n",
-    "                                      solver='saga',\r\n",
-    "                                      multi_class='multinomial',\r\n",
-    "                                      max_iter=10000),\r\n",
-    "    'L2 logistic (Multinomial)': LogisticRegression(C=C, penalty='l2',\r\n",
-    "                                                    solver='saga',\r\n",
-    "                                                    multi_class='multinomial',\r\n",
-    "                                                    max_iter=10000),\r\n",
-    "    'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2',\r\n",
-    "                                            solver='saga',\r\n",
-    "                                            multi_class='ovr',\r\n",
-    "                                            max_iter=10000),\r\n",
-    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,\r\n",
-    "                      random_state=0)\r\n",
-    "}\r\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy (train) for L1 logistic: 80.7% \n",
-      "Accuracy (train) for L2 logistic (Multinomial): 80.9% \n",
-      "Accuracy (train) for L2 logistic (OvR): 80.7% \n",
-      "Accuracy (train) for Linear SVC: 79.9% \n"
-     ]
-    }
-   ],
-   "source": [
-    "n_classifiers = len(classifiers)\r\n",
-    "\r\n",
-    "for index, (name, classifier) in enumerate(classifiers.items()):\r\n",
-    "    classifier.fit(X_train, y_train)\r\n",
-    "\r\n",
-    "    y_pred = classifier.predict(X_test)\r\n",
-    "    accuracy = accuracy_score(y_test, y_pred)\r\n",
-    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\r\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "dd61f40108e2a19f4ef0d3ebbc6b6eea57ab3c4bc13b15fe6f390d3d86442534"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.8.5 64-bit ('onnxwine': conda)",
-   "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": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/4-Classification/1-Introduction/solution/intro-classification.ipynb b/4-Classification/1-Introduction/solution/intro-classification.ipynb
deleted file mode 100644
index 997e18258..000000000
--- a/4-Classification/1-Introduction/solution/intro-classification.ipynb
+++ /dev/null
@@ -1,563 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Build Classification Model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\r\n",
-    "from sklearn.model_selection import train_test_split, cross_val_score\r\n",
-    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\r\n",
-    "from sklearn.svm import SVC\r\n",
-    "import pandas as pd\r\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>almond</th>\n",
-       "      <th>angelica</th>\n",
-       "      <th>anise</th>\n",
-       "      <th>anise_seed</th>\n",
-       "      <th>apple</th>\n",
-       "      <th>apple_brandy</th>\n",
-       "      <th>apricot</th>\n",
-       "      <th>armagnac</th>\n",
-       "      <th>artemisia</th>\n",
-       "      <th>artichoke</th>\n",
-       "      <th>...</th>\n",
-       "      <th>whiskey</th>\n",
-       "      <th>white_bread</th>\n",
-       "      <th>white_wine</th>\n",
-       "      <th>whole_grain_wheat_flour</th>\n",
-       "      <th>wine</th>\n",
-       "      <th>wood</th>\n",
-       "      <th>yam</th>\n",
-       "      <th>yeast</th>\n",
-       "      <th>yogurt</th>\n",
-       "      <th>zucchini</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 380 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
-       "0       0         0      0           0      0             0        0   \n",
-       "1       1         0      0           0      0             0        0   \n",
-       "2       0         0      0           0      0             0        0   \n",
-       "3       0         0      0           0      0             0        0   \n",
-       "4       0         0      0           0      0             0        0   \n",
-       "\n",
-       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
-       "0         0          0          0  ...        0            0           0   \n",
-       "1         0          0          0  ...        0            0           0   \n",
-       "2         0          0          0  ...        0            0           0   \n",
-       "3         0          0          0  ...        0            0           0   \n",
-       "4         0          0          0  ...        0            0           0   \n",
-       "\n",
-       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
-       "0                        0     0     0    0      0       0         0  \n",
-       "1                        0     0     0    0      0       0         0  \n",
-       "2                        0     0     0    0      0       0         0  \n",
-       "3                        0     0     0    0      0       0         0  \n",
-       "4                        0     0     0    0      0       1         0  \n",
-       "\n",
-       "[5 rows x 380 columns]"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformed_feature_df = pd.read_csv(\".data/features_dataset.csv\")\r\n",
-    "transformed_feature_df= transformed_feature_df.drop(['Unnamed: 0'], axis=1)\r\n",
-    "transformed_feature_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>cuisine</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>indian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>indian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>indian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>indian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>indian</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  cuisine\n",
-       "0  indian\n",
-       "1  indian\n",
-       "2  indian\n",
-       "3  indian\n",
-       "4  indian"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformed_label_df = pd.read_csv(\".data/labels_dataset.csv\")\r\n",
-    "transformed_label_df= transformed_label_df.drop(['Unnamed: 0'], axis=1)\r\n",
-    "transformed_label_df.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(transformed_feature_df, transformed_label_df, test_size=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy is 0.8031693077564637\n"
-     ]
-    }
-   ],
-   "source": [
-    "lr = LogisticRegression(multi_class='ovr',solver='lbfgs')\r\n",
-    "model = lr.fit(X_train, np.ravel(y_train))\r\n",
-    "\r\n",
-    "accuracy = model.score(X_test, y_test)\r\n",
-    "print (\"Accuracy is {}\".format(accuracy))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ingredients: Index(['corn'], dtype='object')\n",
-      "cusine: cuisine    thai\n",
-      "Name: 3816, dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "# test an item\r\n",
-    "print(f'ingredients: {X_test.iloc[20][X_test.iloc[20]!=0].keys()}')\r\n",
-    "print(f'cusine: {y_test.iloc[20]}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>0</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>thai</th>\n",
-       "      <td>0.475724</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>chinese</th>\n",
-       "      <td>0.201912</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>japanese</th>\n",
-       "      <td>0.152046</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>korean</th>\n",
-       "      <td>0.110980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>indian</th>\n",
-       "      <td>0.059338</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 0\n",
-       "thai      0.475724\n",
-       "chinese   0.201912\n",
-       "japanese  0.152046\n",
-       "korean    0.110980\n",
-       "indian    0.059338"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#rehsape to 2d array and transpose\r\n",
-    "test= X_test.iloc[20].values.reshape(-1, 1).T\r\n",
-    "# predict with score\r\n",
-    "proba = model.predict_proba(test)\r\n",
-    "classes = model.classes_\r\n",
-    "# create df with classes and scores\r\n",
-    "resultdf = pd.DataFrame(data=proba, columns=classes)\r\n",
-    "\r\n",
-    "# create df to show results\r\n",
-    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\r\n",
-    "topPrediction.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              precision    recall  f1-score   support\n",
-      "\n",
-      "     chinese       0.77      0.70      0.74       239\n",
-      "      indian       0.88      0.88      0.88       240\n",
-      "    japanese       0.76      0.79      0.77       227\n",
-      "      korean       0.86      0.78      0.82       240\n",
-      "        thai       0.75      0.86      0.80       253\n",
-      "\n",
-      "    accuracy                           0.80      1199\n",
-      "   macro avg       0.81      0.80      0.80      1199\n",
-      "weighted avg       0.81      0.80      0.80      1199\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred = model.predict(X_test)\r\n",
-    "print(classification_report(y_test,y_pred))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Try different classifiers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\r\n",
-    "C = 10\r\n",
-    "# Create different classifiers.\r\n",
-    "classifiers = {\r\n",
-    "    'L1 logistic': LogisticRegression(C=C, penalty='l1',\r\n",
-    "                                      solver='saga',\r\n",
-    "                                      multi_class='multinomial',\r\n",
-    "                                      max_iter=10000),\r\n",
-    "    'L2 logistic (Multinomial)': LogisticRegression(C=C, penalty='l2',\r\n",
-    "                                                    solver='saga',\r\n",
-    "                                                    multi_class='multinomial',\r\n",
-    "                                                    max_iter=10000),\r\n",
-    "    'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2',\r\n",
-    "                                            solver='saga',\r\n",
-    "                                            multi_class='ovr',\r\n",
-    "                                            max_iter=10000),\r\n",
-    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,\r\n",
-    "                      random_state=0)\r\n",
-    "}\r\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy (train) for L1 logistic: 79.9% \n",
-      "Accuracy (train) for L2 logistic (Multinomial): 79.7% \n",
-      "Accuracy (train) for L2 logistic (OvR): 79.8% \n",
-      "Accuracy (train) for Linear SVC: 77.9% \n"
-     ]
-    }
-   ],
-   "source": [
-    "n_classifiers = len(classifiers)\r\n",
-    "\r\n",
-    "for index, (name, classifier) in enumerate(classifiers.items()):\r\n",
-    "    classifier.fit(X_train, np.ravel(y_train))\r\n",
-    "\r\n",
-    "    y_pred = classifier.predict(X_test)\r\n",
-    "    accuracy = accuracy_score(y_test, y_pred)\r\n",
-    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\r\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "dd61f40108e2a19f4ef0d3ebbc6b6eea57ab3c4bc13b15fe6f390d3d86442534"
-  },
-  "kernelspec": {
-   "display_name": "Python 3.8.5 64-bit ('onnxwine': conda)",
-   "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": "3.8.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/4-Classification/1-Introduction/solution/notebook.ipynb b/4-Classification/1-Introduction/solution/notebook.ipynb
index 0d08010ca..84f6b3826 100644
--- a/4-Classification/1-Introduction/solution/notebook.ipynb
+++ b/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -11,12 +11,14 @@
    "source": [
     "Install Imblearn which will enable SMOTE. This is a Scikit-Learn package that helps handle imbalanced data when performing classification. (https://imbalanced-learn.org/stable/)"
    ],
-   "cell_type": "markdown",
-   "metadata": {}
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -26,8 +28,8 @@
       "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
       "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
       "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
-      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
       "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
       "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
       "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
       "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
@@ -42,11 +44,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
@@ -56,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -105,7 +106,7 @@
       "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>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 20
+     "execution_count": 5
     }
    ],
    "source": [
@@ -114,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -131,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +148,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 22
+     "execution_count": 7
     }
    ],
    "source": [
@@ -163,24 +164,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffcd13483c8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3d172ccc0>"
       ]
      },
      "metadata": {},
-     "execution_count": 23
+     "execution_count": 8
     },
     {
      "output_type": "display_data",
      "data": {
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      },
      "metadata": {
@@ -194,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -229,40 +230,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def create_ingredient_df(df):\r\n",
-    "    #transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\r\n",
-    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\r\n",
-    "    # drop ingredients that have a 0 sum\r\n",
-    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\r\n",
-    "    # sort df\r\n",
-    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\r\n",
-    "    return ingredient_df\r\n"
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffce8ffb828>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3b0cecc50>"
       ]
      },
      "metadata": {},
-     "execution_count": 26
+     "execution_count": 11
     },
     {
      "output_type": "display_data",
      "data": {
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8P/V/MSIWpuk6oGZHCo2IyRFRiIhC99377siqzMysRN6CqTOVPjq9kXduld/MO8ehV5dWZGZmW8lbMD0EjJHUD0DSXsATwAVp/jjg0TT9IPDZ1K+7pL5p3jmSdpe0B/Cxkv6tWQ4ck6bPK2l/A+izQ3tjZmbbLFfBFBFLgRuBuZIWATcDVwCfkbQY+DTF606knydLqqd4au7I9Cj3KcAfgXnATyLiqXY2+x3gs5KeAvqXtM8BjvTND2ZmXUsRkXUNFa/ngMEx4KLvZV2GWUXwNz9YE0l1EVFo3u6vJCqDoQP7Uut/bGZmZZGrU3lmZmYOJjMzyxUHk5mZ5YqDyczMcsXBZGZmueJgMjOzXHEwmZlZrjiYzMwsVxxMZmaWKw4mMzPLFX8lURnUr2ygZuLsrMswq2j+Dj1r4hGTmZnlSsUHk6RvSDo16zrMzKw8Kv5UXkRc39nbkNQ9Iho7eztmZlZhIyZJX5W0TNJjkn4u6RpJUySdl+Yvl/R1SQsk1Us6PLXvLel3kpZK+omklyT1T/M+JemP6YGAP5bUPbWvk/Qf6YGFx2e202ZmVaZigknSscC5wDDgDGCrh0slayPiaOA24JrU9jXgoYgYAswEDkzrPAIYC5wYEcOBRoqPbwfYA5gXEcMi4rEW6hkvqVZSbeP6hrLso5mZVdapvBOB+yJiA7BB0q9a6XdP+lkHfDxNnwR8DCAifivpb6n9FOAYYL4kgN2A1WleI/DL1oqJiMnAZCg+wXZ7dsjMzLZWScHUURvTz0ba3z8BUyPi31qYt8HXlczMul7FnMoDHgdGS+olqTcwahuXPR9A0keAd6f2B4HzJO2T5u0l6aAy1mxmZtuoYkZMETFf0ixgMfAKUA909OLO14GfS/o08CTwMvBGRKyVdB3wgKRuwCbg88BLZd8BMzPrEEVUzuURSb0jYp2k3YFHgPERsaADy/UEGiNis6TjgdvSzQ5lUSgUora2tlyrMzOrCpLqImKrG9kqZsSUTJZ0JNCL4rWhdkMpORCYnkZF/wQu66wCzcxsx1RUMEXEJ7dzueeBEWUux8zMOkEl3fxgZmZVwMFkZma54mAyM7NccTCZmVmuOJjMzCxXHExmZpYrDiYzM8sVB5OZmeVKRf2BbV7Vr2ygZuLsrMswqzrLbzoz6xKsE3jEZGZmueJgMjOzXKmIYJJ0uaQL0/QUSedt53qGS/poeaszM7NyqohrTBFxe5lWNRwoAP/dfIakXSJic5m2Y2Zm2ymXwZRGR9cAQfHBgH8C1kXEd5r1ux4YDewGPAH8a0SEpIeBecDJwJ7Apen9N4DdJJ0E/DtwBHAIcDDwP5I+A9xGMbw2A1dHxJzO3VszMyuVu1N5koYA1wEjI2IYcGUb3W+JiGMj4iiK4VT6uPVdIuJ9wFXA1yLin8D1wLSIGB4R01K/I4FTI+ITFJ9eGxExFPgEMFVSr1bqHC+pVlJt4/qOPkjXzMzak7tgAkYCMyJiLUBEvNZG35MlzZNUn5YbUjLvnvSzDqhpYx2zIuLNNH0S8J9pu89SfMT6oS0tFBGTI6IQEYXuu/dtZ5fMzKyjcnkqryPSSOZHQCEi/iJpEsUn2zbZmH420vZ+/qNzKjQzs+2RxxHTQ8AYSf0AJO3VSr+mEForqTfQkTv13gD6tDH/UWBc2u6hFB/JvqwjRZuZWXnkLpgiYilwIzBX0iLg5lb6vQ7cASwB7gfmd2D1c4AjJS2UNLaF+T8CuqVTg9OAiyNiYwv9zMyskygisq6h4hUKhaitrc26DDOziiKpLiIKzdtzN2IyM7Pq5mAyM7NccTCZmVmuOJjMzCxXHExmZpYrDiYzM8sVB5OZmeWKg8nMzHLFwWRmZrniYDIzs1yp2G8Xz5P6lQ3UTJyddRlm1omW33Rm1iVUDY+YzMwsVxxMZmaWKw4mMzPLlVwEk6QLJS2WtEjSXZJGp0emPyXp95L2ldRN0vOS9k7LdJP0gqS90+uXkuan14mpzyRJd0p6WNKfJU1I7TWSnpF0h6Slkh6QtFuad4ik30qqk/SopMOzOzJmZtUn82CSNAS4DhgZEcOAK4HHgOMiYgTwC+ArEfEW8J+kJ8wCpwKLImIN8H3guxFxLHAu8JOSTRwOnAa8D/iapB6pfTBwa0QMAV5PywFMBq6IiGOAayg+PLClusdLqpVU27i+YYePg5mZFeXhrryRwIyIWAsQEa9JGgpMkzQA2BV4MfW9E7gP+B5wCfDT1H4qxSfTNq3zXelx6wCz01NoN0paDeyb2l+MiIVpug6oScucAMwoWVfPloqOiMkUQ4yeAwb7aYtmZmWSh2BqyQ+BmyNilqQPAZMAIuIvkl6RNJLiCKhp9NSN4ghrQ+lKUriUPhq9kXf2uXn7bmk9r0fE8LLujZmZdVjmp/KAh4AxkvoBSNoL6AusTPMvatb/JxRP6c2IiMbU9gBwRVMHSdsVLBHxd+BFSWPSeiRp2Pasy8zMtk/mwRQRS4EbgbmSFgE3UxwhzZBUB6xttsgsoDfvnMYDmAAU0g0UTwOX70BJ44BLUy1LgbN3YF1mZraNFFFZl0ckFSje6PD+rGtp0nPA4Bhw0feyLsPMOpG/+aH8JNVFRKF5e16vMbVI0kTgs7xzbSkXhg7sS60/tGZmZZH5qbxtERE3RcRBEfFY1rWYmVnnqKhgMjOznZ+DyczMcsXBZGZmueJgMjOzXHEwmZlZrjiYzMwsVxxMZmaWKw4mMzPLFQeTmZnlSkV9JVFe1a9soGbi7KzLMLOdWDV9V59HTGZmlitVEUySJkh6RtLf0hfBttbvYkm3dGVtZma2pWo5lfc54NSIWJF1IWZm1radfsQk6XbgYOA3kr7YNCKSNEbSEkmLJD1Sssh7JP1W0vOSvpVJ0WZmVWynD6aIuBz4K3Ay8LeSWdcDp0XEMOCskvbhwFhgKDBW0gEtrVfSeEm1kmob1zd0TvFmZlVopw+mNjwOTJF0GdC9pP3BiGiIiA3A08BBLS0cEZMjohARhe679+2Ccs3MqkPVBlMaSV0HHADUSeqXZm0s6dZI9VyHMzPLhar9pSvpkIiYB8yTdAbFgDIzs4xV7YgJ+LakeklLgCeARVkXZGZmVTJiioiaNDklvYiIj7fQ9e35qc+oTi3MzMy2UhXB1NmGDuxLbRV9XYiZWWeq5lN5ZmaWQw4mMzPLFQeTmZnlioPJzMxyxcFkZma54mAyM7NccTCZmVmuOJjMzCxXHExmZpYr/uaHMqhf2UDNxNlZl2Fm1qWWd9I33njEZGZmueJgMjOzXKnaYJJ0saRb0vTlki7MuiYzM6vSa0ySttjviLg9q1rMzGxLFR1Mkr4KfApYA/wFqAMagPHArsALwKcjYr2kKcAGYATwOLC4ZD2TgHUR8R1J/wu4Hdib4qPVx0TEn7pqn8zMql3FnsqTdCxwLjAMOAMopFn3RMSxETEMeAa4tGSx/YETIuLqNlZ9N3BrWv4EYFUr2x8vqVZSbeP6hh3cGzMza1LJI6YTgfsiYgOwQdKvUvtRkm4A9gR6A/eXLDMjIhpbW6GkPsDAiLgXIK27RRExGZgM0HPA4NihPTEzs7dV7IipDVOAL0TEUODrQK+Sef/IpCIzM+uwSg6mx4HRknpJ6g2MSu19gFWSegDjtmWFEfEGsELSOQCSekravZxFm5lZ2yo2mCJiPjCL4k0MvwHqKd748FVgHsXgenY7Vv1pYIKkxcATwH5lKdjMzDpEEZV7eURS74hYl0Y1jwDjI2JBV9dRKBSitra2qzdrZlbRJNVFRKF5eyXf/AAwWdKRFK8jTc0ilMzMrLwqOpgi4pNZ12BmZuVVsdeYzMxs5+RgMjOzXHEwmZlZrlT0XXl5IekNYFnWdWyH/sDarIvYDq67a7nurlUtda8FiIjTm8+o6JsfcmRZS7c85p2kWtfddVx313LdXaucdftUnpmZ5YqDyczMcsXBVB6Tsy5gO7nuruW6u5br7lplq9s3P5iZWa54xGRmZrniYDIzs1xxMO0ASadLWibpBUkTs66nNZIOkDRH0tOSlkq6MrVPkrRS0sL0+mjWtTYnabmk+lRfbWrbS9LvJD2ffr476zpLSTqs5JgulPR3SVfl9XhLulPSaklLStpaPMYq+kH6zC+WdHTO6v62pGdTbfdK2jO110h6s+TY356zulv9bEj6t3S8l0k6LZuqW617WknNyyUtTO07drwjwq/teAHdgT8BBwO7AouAI7Ouq5VaBwBHp+k+wHPAkcAk4Jqs62un9uVA/2Zt3wImpumJwDezrrOdz8nLwEF5Pd7AB4CjgSXtHWPgoxSffybgOGBezur+CLBLmv5mSd01pf1yeLxb/Gykf6eLgJ7AoPQ7p3te6m42/z+A68txvD1i2n7vA16IiD9HxD+BXwBnZ1xTiyJiVaRHgkTxKb3PAAOzrWqHnA1MTdNTgXMyrKU9pwB/ioiXsi6kNRHxCPBas+bWjvHZwM+i6A/AnpIGdE2lW2qp7oh4ICI2p7d/APbv8sLa0crxbs3ZwC8iYmNEvAi8QPF3T5drq25JAs4Hfl6ObTmYtt9A4C8l71dQAb/sJdUAIyg+5RfgC+m0x515OyWWBPCApDpJ41PbvhGxKk2/DOybTWkdcgFb/mPN+/Fu0toxrqTP/SUUR3dNBkl6StJcSe/Pqqg2tPTZqJTj/X7glYh4vqRtu4+3g6mKSOoN/BK4KiL+DtwGHAIMB1ZRHIrnzUkRcTRwBvB5SR8onRnF8wa5/JsHSbsCZwEzUlMlHO+t5PkYt0bStcBm4O7UtAo4MCJGAFcD/yXpXVnV14KK/GyU+ARb/gdsh463g2n7rQQOKHm/f2rLJUk9KIbS3RFxD0BEvBIRjRHxFnAHGZ0iaEtErEw/VwP3UqzxlabTR+nn6uwqbNMZwIKIeAUq43iXaO0Y5/5zL+liYBQwLoUq6VTYq2m6juK1mkMzK7KZNj4blXC8dwE+DkxratvR4+1g2n7zgcGSBqX/GV8AzMq4phal87//F3gmIm4uaS+9NvAxYEnzZbMkaQ9JfZqmKV7YXkLxOF+Uul0E3JdNhe3a4n+ReT/ezbR2jGcBF6a7844DGkpO+WVO0unAV4CzImJ9Sfvekrqn6YOBwcCfs6lya218NmYBF0jqKWkQxbr/2NX1teNU4NmIWNHUsMPHO4u7O3aWF8U7lJ6j+L+Ba7Oup406T6J4KmYxsDC9PgrcBdSn9lnAgKxrbVb3wRTvSFoELG06xkA/4EHgeeD3wF5Z19pC7XsArwJ9S9pyebwphucqYBPFaxiXtnaMKd6Nd2v6zNcDhZzV/QLFazJNn/PbU99z02doIbAAGJ2zulv9bADXpuO9DDgjT3Wn9inA5c367tDx9lcSmZlZrvhUnpmZ5YqDyczMcsXBZGZmueJgMjOzXHEwmZlZrjiYzMwsVxxMZmaWK/8fnSxrKwF+wYgAAAAASUVORK5CYII=\n"
      },
      "metadata": {
@@ -277,24 +278,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffcf8070c18>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3a0bef668>"
       ]
      },
      "metadata": {},
-     "execution_count": 27
+     "execution_count": 12
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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      },
      "metadata": {
@@ -309,24 +310,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffce9038978>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3e14ebc50>"
       ]
      },
      "metadata": {},
-     "execution_count": 28
+     "execution_count": 13
     },
     {
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
@@ -341,24 +342,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffcc9efe160>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3e15230f0>"
       ]
      },
      "metadata": {},
-     "execution_count": 29
+     "execution_count": 14
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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      },
      "metadata": {
@@ -373,24 +374,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffce906f710>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3b0d1fe10>"
       ]
      },
      "metadata": {},
-     "execution_count": 30
+     "execution_count": 15
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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QYmpX1VE5cWaxwzBrV89ddlyxQ7AS4hmpmZlZDk6kOzhJ/5VeDG5mZjsgL+3uwCQJOD4iNhc7FjMza5xnpDsYSZWSlkv6BbAY2CRpz7TvdEmLJC2UdGsq6yPp15Lmp5+Dixm/mVmp8Yx0xzQAOCMi/ijpOQBJg4CLgdER8Yqk3VPdq4EfR8SjkvYBZgOfLGxM0nhgPED33fp00CmYmZUGJ9Id0/MR8ccGZUcA0yPiFYCIeC2VHwXsl60CA7CbpF4Rsba+ICImA5MBelQM8JvczczakBPpjuntVtTtBhwUEevbKxgzM2uar5F2HnOAMZL2AChY2v09MKG+kqRhRYjNzKxkOZF2EhGxBLgUeEjSQuDKtOt8oCrdhLQUOKdYMZqZlSIv7e5gIuI5YHDB58qC7anA1Ab1XwHGdlB4ZmbWgBNpiRnSt5xqPz7NzKzNeGnXzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAcnUjMzsxycSM3MzHJwIjUzM8vBidTMzCwHJ1IzM7Mc/IjAElO7qo7KiTOLHYZZUT3nx2RaG/KM1MzMLAcnUjMzsxy6XCKVVClpcSPlD0qq2o72zpR0XdtEZ2ZmXU2XS6QGknzt28ysg3TVRLqTpNslLZM0Q9IuhTsl3SipWtISSd8vKB8p6XFJCyU9Kal3g+OOkzRP0p6NdSppiqSbUtvPSDo+lXeXdIWk+ZIWSfrXVH64pIclzZS0PB3bLe1bK+nHKcYHJPVJ5ftKmiWpRtIjkgY26PsJ4IcN4hqfYqretK4u9+CamdkWXTWRfgK4ISI+CbwJ/O8G+y+KiCpgf+AwSftL2hm4E/haRAwFjgL+Xn+ApC8AE4FjI+KVZvquBA4EjgNuktQTOBuoi4iRwEjgq5L6p/oHAhOA/YB9gX9O5bsC1RExCHgI+F4qnwxMiIgRwIXADQV99wNGR8Q3CgOKiMkRURURVd13KW8mdDMza62uugT4QkQ8lrZvA85vsP9USePJzr+CLIkFsDoi5gNExJsAkgCOAKqAz9SXN2NaRGwG/izpWWAg8Blgf0mnpDrlwADgH8CTEfFs6utXwCHADGAzWWKvP4ffSOoFjAamp7gAehT0PT0iNm0jPjMza0NdNZFGU5/TTPBCYGREvC5pCtBzG+39Ffgo8HGgejv6FtkscnbhDkmHNxdrI+XdgDciYlgTdd7eRmxmZtbGuurS7j6SRqXtLwGPFuzbjSzh1EnaC/hcKl8OVEgaCSCpd8FNO88DJwO/kDRoG32PkdRN0r5kyXc5MBs4V1JZavvjknZN9Q+U1D9dGx1bEGs3oH4G+yXg0TQbXiFpTGpHkoa2dFDMzKztddVEuhz4N0nLgA8AN9bviIiFwALgT8AvgcdS+T/IEtm1khYC91MwU42IPwHjyJZV922m7/8GngR+B5wTEeuBnwFLgafSV3N+wpbVgPnAdcAyYAVwVyp/myzJLiZbWv7PVD4OODvFuAQ4sVUjY2ZmbUoRTa0kWmulZeL7ImJGC+sfDlwYEcc3sm9tRPRq2wihqqoqqqu3tTptZmaFJNWkm1Tfo6vOSM3MzDpEV73ZqF1JuggY06B4ekSc2Zp2IuJB4MEm9rX5bNTMzNqeE+l2iIhLgUuLHYeZmRWfl3bNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAd/j7TE1K6qo3LizGKHYWbNeO6y44odgrWCZ6RmZmY5OJGamZnl4ETayUk6R9LpaXuKpFO2dYyZmbUdXyPt5CLipmLHYGZWykp6RippV0kzJS2UtFjSWEkjJD0kqUbSbEkVqe75kpZKWiTpjlR2oKR5khZIelzSJ1L5mZLulnS/pOcknSfpG6neHyXtnurtK2lW6usRSQObibVS0pzU/wOS9knlkyRduI3zHC+pWlL1pnV1bTV8ZmZGiSdS4BjgxYgYGhGDgVnAtcApETECuJktb3mZCAyPiP2Bc1LZn4BPR8Rw4P8A/7eg7cHAPwMjUxvrUr15wOmpzmRgQurrQuCGZmK9Fpia+r8duKalJxkRkyOiKiKquu9S3tLDzMysBUp9abcW+P+SLgfuA14nS4D3SwLoDqxOdRcBt0u6G7g7lZUDUyUNAAIoK2h7bkS8BbwlqQ74bUGf+0vqBYwGpqe+AHo0E+sossQMcCvww9afrpmZtbWSTqQR8YykA4BjgUuAOcCSiBjVSPXjgEOBE4CLJA0BfkCWML8gqZKtX9K9oWB7c8HnzWTj3g14IyKGtdkJmZlZhyvppV1JHyZbcr0NuAL4FNBH0qi0v0zSIEndgL0jYi7wbbKZaK/0e1Vq7szW9B0RbwIrJI1JfUnS0GYOeRz4YtoeBzzSmv7MzKx9lPSMFBgCXCFpM7AROBd4B7hGUjnZ+FwFPAPclsoEXBMRb0j6IdnS7sXA9jwuaBxwYzq+DLgDWNhE3QnALZK+CawBvrId/TGkbznVfmqKmVmbUUQUOwbrQFVVVVFdXV3sMMzMOhVJNRFR1di+kl7aNTMzy6vUl3Z3OJIuAsY0KJ4eEZc2Vt/MzIrLiXQHkxKmk6aZWSfhpV0zM7McnEjNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcvDXX0pM7ao6Kiduz9MMzayUPOdHibaYZ6RmZmY5OJGamZnl4ERqZmaWgxOpmZlZDk6kBSTtKmmmpIWSFksaK+lISQsk1Uq6WVIPSUdIurvguKMl3dVEm90lTUnt1Ur691T+VUnzU1+/lrRLKp8i6ZSC49cWbH87tbFQ0mWpbF9JsyTVSHpE0sD2Gh8zM3svJ9KtHQO8GBFDI2IwMAuYAoyNiCFkdzmfC8wFBkrqk477CnBzE20OA/pGxODUxi2p/DcRMTIihgLLgLObC0zS54ATgU+lY36Ydk0GJkTECOBC4IZGjh0vqVpS9aZ1ddseBTMzazEn0q3VAkdLulzSp4FKYEVEPJP2TwUOjext6LcCX5b0fmAU8Lsm2nwW+KikayUdA7yZygenGWQtMA4YtI3YjgJuiYh1ABHxmqRewGhguqSngZ8AFQ0PjIjJEVEVEVXddylvyTiYmVkL+XukBSLiGUkHAMcClwBzmql+C/BbYD3Z+0LfaaLN1yUNBT4LnAOcCpxFNtM9KSIWSjoTODwd8g7pPziSugE7NxNDN+CNiBjWkvMzM7O25xlpAUkfBtZFxG3AFWQzzUpJH0tVTgMeAoiIF4EXgYvZslzbWJt7At0i4tep7gFpV29gtaQyshlpveeAEWn780BZ2r4f+ErBtdTdI+JNYIWkMalMKWmbmVkH8Yx0a0OAKyRtBjaSXQ8tJ1s63QmYD9xUUP92oE9ELGumzb7ALWl2CfAf6fd3gSeANel371T+U+AeSQvJrtG+DRARsyQNA6ol/QP4L+A7ZEn4RkkXkyXdO4CF23n+ZmbWSsou99n2kHQdsCAifl7sWFqqqqoqqqurix2GmVmnIqkmIqoa2+cZ6XaSVEM2W7yg2LGYmVnxOJFup/R1k61IegLo0aD4tIio7ZiozMysozmRtqGI+FSxYzAzs47lu3bNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAd/j7TE1K6qo3LizGKHYWZdzHOXHVfsEIrGM1IzM7McnEjNzMxycCI1MzPLwYm0lSSdLmmRpIWSbpV0gqQnJC2Q9AdJe0nqJunPkvqkY7pJ+oukPunn15Lmp5+DU51Jkm6W9KCkZyWdn8orJS2T9FNJSyT9XtL70r59Jc2SVCPpEUkDizcyZmalyYm0FSQNAi4GjoiIocDXgEeBgyJiONlLtb8VEZuB28heug1wFLAwItYAVwM/joiRwMnAzwq6GAh8FjgQ+J6kslQ+ALg+IgYBb6TjACYDE9KbaC4Ebmgi7vGSqiVVb1pXl3sczMxsC9+12zpHANMj4hWAiHhN0hDgTkkVwM7AilT3ZuAe4CrgLOCWVH4UsJ+k+jZ3k9Qrbc+MiA3ABkkvA3ul8hUR8XTargEq0zGjgekFbTV8hRspzslkSZceFQP8JnczszbkRJrftcCVEXGvpMOBSQAR8YKklyQdQTbDrJ+ddiObwa4vbCQlww0FRZvY8ufTsPx9qZ03ImJYm56NmZm1ipd2W2cOMEbSHgCSdgfKgVVp/xkN6v+MbIl3ekRsSmW/BybUV5C0XYkwIt4EVkgak9qRpKHb05aZmW0/J9JWiIglwKXAQ5IWAleSzUCnS6oBXmlwyL1AL7Ys6wKcD1SlG5aWAufkCGkccHaKZQlwYo62zMxsOyjCl8zai6QqshuLPl3sWOr1qBgQFWdcVewwzKyL6epPNpJUExFVje3zNdJ2ImkicC5bro3uEIb0Lae6i/+FNzPrSF7abScRcVlEfCQiHi12LGZm1n6cSM3MzHJwIjUzM8vBidTMzCwHJ1IzM7McnEjNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcvAjAktM7ao6KifOLHYYZlYCuvrzd+t5RmpmZpaDE2mRSaqUtDhtHy7pvrT9+fTgezMz24F5aXcHFRH3kr3P1MzMdmCekeYkaVdJMyUtlLRY0lhJIyU9nsqelNQ7zTwfkfRU+hm9jXbPlHRd2q6UNCe9DPwBSfuk8imSrkl9PSvplI44ZzMz28Iz0vyOAV6MiOMAJJUDC4CxETFf0m7A34GXgaMjYr2kAcCvgEZfEtuIa4GpETFV0lnANcBJaV8FcAgwkGwGO6PhwZLGA+MBuu/WZ/vO0szMGuUZaX61wNGSLpf0aWAfYHVEzAeIiDcj4h2gDPippFpgOrBfK/oYBfwybd9Kljjr3R0RmyNiKbBXYwdHxOSIqIqIqu67lLfq5MzMrHmekeYUEc9IOgA4FrgEmNNE1X8HXgKGkv0HZn0bhbChYFtt1KaZmbWQZ6Q5SfowsC4ibgOuAD4FVEgamfb3lrQTUE42U90MnAZ0b0U3jwNfTNvjgEfaKn4zM8vHM9L8hgBXSNoMbATOJZsZXivpfWTXR48CbgB+Lel0YBbwdiv6mADcIumbwBrgK20Yv5mZ5aCIKHYM1oF6VAyIijOuKnYYZlYCutKTjSTVRESjN4h6RlpihvQtp7oL/eU2Mys2XyM1MzPLwYnUzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAcnUjMzsxycSM3MzHJwIjUzM8vBTzYqMbWr6qicOLPYYZiZdaj2fFyhZ6RmZmY5OJGamZnl4ETaSUg6U9J1afuc9Do2MzMrMl8j7QTSi8HfFRE3FSsWMzPbmhNpB5L0XeDLZC/nfgGoAeqA8cDOwF+A0yJinaQpwHpgOPAYsKignUnA2oj4kaSPATcBfYBNwJiI+GtHnZOZWanz0m4HkTQSOBkYCnwOqH9B7G8iYmREDAWWAWcXHNYPGB0R32im6duB69Pxo4HVjfQ9XlK1pOpN6+ra4GzMzKyeZ6Qd52DgnohYD6yX9NtUPljSJcD7gV7A7IJjpkfEpqYalNQb6BsRdwGktt8jIiYDkwF6VAyI3GdiZmbv8oy0+KYA50XEEOD7QM+CfW8XJSIzM2sxJ9KO8xhwgqSeknoBx6fy3sBqSWXAuNY0GBFvASslnQQgqYekXdoyaDMza54TaQeJiPnAvWQ3Df0OqCW70ei7wBNkifZP29H0acD5khYBjwMfapOAzcysRRThS2YdRVKviFibZo0PA+Mj4qmOjKGqqiqqq6s7skszs05PUk1EVDW2zzcbdazJkvYjuw46taOTqJmZtT0n0g4UEV8qdgxmZta2fI3UzMwsBydSMzOzHJxIzczMcvBduyVG0lvA8mLHsQPZE3il2EHsQDweW3gstlbq4/GRiOjT2A7fbFR6ljd1C3cpklTt8djC47GFx2JrHo+meWnXzMwsBydSMzOzHJxIS8/kYgewg/F4bM3jsYXHYmsejyb4ZiMzM7McPCM1MzPLwYnUzMwsByfSEiLpGEnLJf1F0sRix9MRJN0s6WVJiwvKdpd0v6Q/p98fSOWSdE0an0WSDihe5G1P0t6S5kpaKmmJpK+l8pIbj/Re4CclLUxj8f1U3l/SE+mc75S0cyrvkT7/Je2vLLtjiJIAAALmSURBVGb87UVSd0kLJN2XPpf0eLSUE2mJkNQduB74HLAf8C/pTTRd3RTgmAZlE4EHImIA8ED6DNnYDEg/44EbOyjGjvIOcEFE7AccBPxb+jtQiuOxATgiIoYCw4BjJB0EXA78OCI+BrwOnJ3qnw28nsp/nOp1RV8DlhV8LvXxaBEn0tJxIPCXiHg2Iv4B3AGcWOSY2l1EPAy81qD4RGBq2p4KnFRQ/ovI/BF4v6SKjom0/UXE6vpX90XEW2T/YPalBMcjndPa9LEs/QRwBDAjlTcci/oxmgEcKUkdFG6HkNQPOA74WfosSng8WsOJtHT0BV4o+LwylZWivSJiddr+G7BX2i6ZMUpLccOBJyjR8UjLmE8DLwP3A38F3oiId1KVwvN9dyzS/jpgj46NuN1dBXwL2Jw+70Fpj0eLOZFaSYvs+18l9R0wSb2AXwNfj4g3C/eV0nhExKaIGAb0I1uxGVjkkIpG0vHAyxFRU+xYOiMn0tKxCti74HO/VFaKXqpfoky/X07lXX6MJJWRJdHbI+I3qbhkxwMgIt4A5gKjyJav659BXni+745F2l8OvNrBobang4HPS3qO7LLPEcDVlO54tIoTaemYDwxId+HtDHwRuLfIMRXLvcAZafsM4J6C8tPT3aoHAXUFS56dXrqG9XNgWURcWbCr5MZDUh9J70/b7wOOJrtmPBc4JVVrOBb1Y3QKMCe60NNsIuI/IqJfRFSS/dswJyLGUaLj0Vp+slEJkXQs2XWQ7sDNEXFpkUNqd5J+BRxO9gqol4DvAXcD04B9gOeBUyPitZRoriO7y3cd8JWIqC5G3O1B0iHAI0AtW66DfYfsOmlJjYek/clululONqGYFhH/KemjZDOy3YEFwJcjYoOknsCtZNeVXwO+GBHPFif69iXpcODCiDje49EyTqRmZmY5eGnXzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAcnUjMzsxycSM3MzHL4Hx9S2FkSapv/AAAAAElFTkSuQmCC\n"
      },
      "metadata": {
@@ -412,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -445,7 +446,7 @@
       "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>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 31
+     "execution_count": 16
     }
    ],
    "source": [
@@ -456,14 +457,14 @@
   },
   {
    "source": [
-    "Balance data with SMOTE oversamplling to the highest class. Read more here: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html"
+    "Balance data with SMOTE oversampling to the highest class. Read more here: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html"
    ],
    "cell_type": "markdown",
    "metadata": {}
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -473,14 +474,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
      "text": [
-      "new label count: chinese     799\nkorean      799\njapanese    799\nthai        799\nindian      799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+      "new label count: korean      799\nchinese     799\nindian      799\njapanese    799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
      ]
     }
    ],
@@ -491,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -524,23 +525,16 @@
       "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>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 34
+     "execution_count": 20
     }
    ],
    "source": [
     "transformed_feature_df.head()"
    ]
   },
-  {
-   "source": [
-    "todo: explain this concatenation?"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -568,7 +562,7 @@
        "4           0         0          0  ...        0            0           0   \n",
        "...       ...       ...        ...  ...      ...          ...         ...   \n",
        "3990        0         0          0  ...        0            0           0   \n",
-       "3991        0         0          0  ...        0            0           1   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
        "3992        0         0          0  ...        0            0           0   \n",
        "3993        0         0          0  ...        0            0           0   \n",
        "3994        0         0          0  ...        0            0           0   \n",
@@ -588,10 +582,10 @@
        "\n",
        "[3995 rows x 381 columns]"
       ],
-      "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>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+      "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>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 35
+     "execution_count": 21
     }
    ],
    "source": [
@@ -599,6 +593,46 @@
     "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
     "transformed_df"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [
+    "Save the file for future use"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisine.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/4-Classification/2-Discriminative/README.md b/4-Classification/2-Classifiers-1/README.md
similarity index 100%
rename from 4-Classification/2-Discriminative/README.md
rename to 4-Classification/2-Classifiers-1/README.md
diff --git a/4-Classification/2-Discriminative/assignment.md b/4-Classification/2-Classifiers-1/assignment.md
similarity index 100%
rename from 4-Classification/2-Discriminative/assignment.md
rename to 4-Classification/2-Classifiers-1/assignment.md
diff --git a/4-Classification/2-Discriminative/translations/README.es.md b/4-Classification/2-Classifiers-1/notebook.ipynb
similarity index 100%
rename from 4-Classification/2-Discriminative/translations/README.es.md
rename to 4-Classification/2-Classifiers-1/notebook.ipynb
diff --git a/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..aeba09710
--- /dev/null
+++ b/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Build Classification Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "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>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "recipes_df = pd.read_csv(\"../../data/cleaned_cuisine.csv\")\n",
+    "recipes_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ],
+   "source": [
+    "recipes_label_df = recipes_df['cuisine']\n",
+    "recipes_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "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>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "source": [
+    "recipes_feature_df = recipes_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "recipes_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(recipes_feature_df, recipes_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.810675562969141\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='lbfgs')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['bean', 'coriander', 'cumin', 'fenugreek', 'pepper', 'turmeric',\n       'vegetable_oil'],\n      dtype='object')\ncusine: thai\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[20][X_test.iloc[20]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[20]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "indian    0.530435\n",
+       "thai      0.344293\n",
+       "japanese  0.108792\n",
+       "chinese   0.015001\n",
+       "korean    0.001480"
+      ],
+      "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>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>indian</th>\n      <td>0.530435</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.344293</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.108792</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.015001</td>\n    </tr>\n    <tr>\n      <th>korean</th>\n      <td>0.001480</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\r\n",
+    "test= X_test.iloc[20].values.reshape(-1, 1).T\r\n",
+    "# predict with score\r\n",
+    "proba = model.predict_proba(test)\r\n",
+    "classes = model.classes_\r\n",
+    "# create df with classes and scores\r\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\r\n",
+    "\r\n",
+    "# create df to show results\r\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\r\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.67      0.70       231\n      indian       0.91      0.90      0.90       255\n    japanese       0.77      0.82      0.79       260\n      korean       0.83      0.83      0.83       220\n        thai       0.79      0.83      0.81       233\n\n    accuracy                           0.81      1199\n   macro avg       0.81      0.81      0.81      1199\nweighted avg       0.81      0.81      0.81      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Try different classifiers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\r\n",
+    "C = 10\r\n",
+    "# Create different classifiers.\r\n",
+    "classifiers = {\r\n",
+    "    'L1 logistic': LogisticRegression(C=C, penalty='l1',\r\n",
+    "                                      solver='saga',\r\n",
+    "                                      multi_class='multinomial',\r\n",
+    "                                      max_iter=10000),\r\n",
+    "    'L2 logistic (Multinomial)': LogisticRegression(C=C, penalty='l2',\r\n",
+    "                                                    solver='saga',\r\n",
+    "                                                    multi_class='multinomial',\r\n",
+    "                                                    max_iter=10000),\r\n",
+    "    'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2',\r\n",
+    "                                            solver='saga',\r\n",
+    "                                            multi_class='ovr',\r\n",
+    "                                            max_iter=10000),\r\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,\r\n",
+    "                      random_state=0)\r\n",
+    "}\r\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for L1 logistic: 79.4% \n",
+      "Accuracy (train) for L2 logistic (Multinomial): 79.2% \n",
+      "Accuracy (train) for L2 logistic (OvR): 80.2% \n",
+      "Accuracy (train) for Linear SVC: 79.1% \n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\r\n",
+    "\r\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\r\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\r\n",
+    "\r\n",
+    "    y_pred = classifier.predict(X_test)\r\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\r\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\r\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "dd61f40108e2a19f4ef0d3ebbc6b6eea57ab3c4bc13b15fe6f390d3d86442534"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "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"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/4-Classification/3-Generative/translations/README.es.md b/4-Classification/2-Classifiers-1/translations/README.es.md
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rename to 4-Classification/2-Classifiers-1/translations/README.es.md
diff --git a/4-Classification/3-Generative/README.md b/4-Classification/3-Classifiers-2/README.md
similarity index 100%
rename from 4-Classification/3-Generative/README.md
rename to 4-Classification/3-Classifiers-2/README.md
diff --git a/4-Classification/3-Generative/assignment.md b/4-Classification/3-Classifiers-2/assignment.md
similarity index 100%
rename from 4-Classification/3-Generative/assignment.md
rename to 4-Classification/3-Classifiers-2/assignment.md
diff --git a/4-Classification/3-Classifiers-2/notebook.ipynb b/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
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 # Getting Started with Classification
 ## Regional topic: Delicious Asian Recipes 🍜
 
-In Asia, food traditions are extremely diverse, and very delicious! Let's look at data about regional recipes to try to guess where they originated.
+In Asia and India, food traditions are extremely diverse, and very delicious! Let's look at data about regional recipes to try to guess where they originated.
 
 ![Thai food seller](./images/thai-food.jpg)
 > Photo by <a href="https://unsplash.com/@changlisheng?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Lisheng Chang</a> on <a href="https://unsplash.com/s/photos/asian-food?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
   
-  
 ## What you will learn
 
-In this section, you will build on the skills you learned in Lesson 1 (Regression) to learn about more classifiers you can use that will help you learn about your data.
+In this section, you will build on the skills you learned in Lesson 1 (Regression) to learn about other classifiers you can use that will help you learn about your data.
 ## Lessons
 
 1. [Introduction to Classification](1-Introduction/README.md)
-2. [Build a Discriminative Model](2-Discriminative/README.md)
-3. [Build a Generative Model](3-Generative/README.md)
+2. [More Classifiers](2-Classifiers-1/README.md)
+3. [Yet Other Classifiers](3-Classifiers-2/README.md)
 4. [Applied ML: Build a Web App](4-Applied/README.md)
 ## Credits