ML-For-Beginners/translations/en/4-Classification/1-Introduction/README.md

Introduction to classification

In these four lessons, you will dive into one of the core areas of traditional machine learning: classification. We'll explore various classification algorithms using a dataset about the diverse and delicious cuisines of Asia and India. Get ready to whet your appetite!

Celebrate pan-Asian cuisines in these lessons! Image by Jen Looper

Classification is a type of supervised learning that shares many similarities with regression techniques. Machine learning is all about predicting values or assigning labels to data using datasets, and classification typically falls into two categories: binary classification and multiclass classification.

🎥 Click the image above for a video: MIT's John Guttag introduces classification

Key points to remember:

• Linear regression helps predict relationships between variables and make accurate predictions about where a new data point might fall relative to a line. For example, you could predict the price of a pumpkin in September versus December.
• Logistic regression helps identify "binary categories": at a certain price point, is this pumpkin orange or not-orange?

Classification uses different algorithms to determine how to assign a label or class to a data point. In this lesson, we'll use cuisine data to see if we can predict the cuisine of origin based on a set of ingredients.

Pre-lecture quiz

This lesson is available in R!

Introduction

Classification is a fundamental task for machine learning researchers and data scientists. From simple binary classification ("is this email spam or not?") to complex image classification and segmentation using computer vision, the ability to sort data into categories and analyze it is invaluable.

To put it more scientifically, classification involves creating a predictive model that maps the relationship between input variables and output variables.

Binary vs. multiclass problems for classification algorithms to handle. Infographic by Jen Looper

Before we start cleaning, visualizing, and preparing our data for machine learning tasks, let's learn about the different ways machine learning can be used to classify data.

Derived from statistics, classification in traditional machine learning uses features like smoker, weight, and age to predict the likelihood of developing a certain disease. As a supervised learning technique similar to regression, classification uses labeled data to train algorithms to classify and predict features (or 'labels') of a dataset and assign them to a group or outcome.

✅ Take a moment to imagine a dataset about cuisines. What could a multiclass model answer? What could a binary model answer? For instance, could you predict whether a given cuisine is likely to use fenugreek? Or, if you were handed a grocery bag containing star anise, artichokes, cauliflower, and horseradish, could you determine if you could make a typical Indian dish?

🎥 Click the image above for a video. The premise of the show 'Chopped' is the 'mystery basket,' where chefs must create dishes using random ingredients. Imagine how helpful a machine learning model could be!

Hello 'classifier'

The question we want to answer with this cuisine dataset is a multiclass question, as we have several possible national cuisines to consider. Given a set of ingredients, which of these classes does the data belong to?

Scikit-learn provides several algorithms for classifying data, depending on the type of problem you're solving. In the next two lessons, you'll explore some of these algorithms.

Exercise - clean and balance your data

Before diving into the project, the first step is to clean and balance your data to achieve better results. Start with the blank notebook.ipynb file in the root of this folder.

The first thing you'll need to install is imblearn. This Scikit-learn package helps balance datasets (you'll learn more about this shortly).

1. Install imblearn using pip install:

   pip install imblearn
   

2. Import the necessary packages to load and visualize your data, and also import SMOTE from imblearn.

   import pandas as pd
   import matplotlib.pyplot as plt
   import matplotlib as mpl
   import numpy as np
   from imblearn.over_sampling import SMOTE
   

   You're now ready to import the data.

3. Import the data:

   df  = pd.read_csv('../data/cuisines.csv')
   

   Use read_csv() to load the contents of the cuisines.csv file into the variable df.

4. Check the shape of the data:

   df.head()
   

   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        |
   

5. Get information about the data using info():

   df.info()
   

   The output looks like this:

   <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
   

Exercise - learning about cuisines

Now the fun begins! Let's explore the distribution of data across cuisines.

1. Plot the data as horizontal bars using barh():

   df.cuisine.value_counts().plot.barh()
   

   

   There are a limited number of cuisines, but the data distribution is uneven. You can fix this! Before doing so, let's explore further.

2. Check how much data is available for each cuisine and print the results:

   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}')
   

   The output looks like this:

   thai df: (289, 385)
   japanese df: (320, 385)
   chinese df: (442, 385)
   indian df: (598, 385)
   korean df: (799, 385)
   

Discovering ingredients

Now let's dig deeper into the data to identify typical ingredients for each cuisine. You'll need to clean out recurring data that might cause confusion between cuisines.

1. Create a Python function create_ingredient() to generate an ingredient dataframe. This function will drop an unhelpful column and sort ingredients by their count:

   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
   

   Use this function to identify the top ten most popular ingredients for each cuisine.

2. Call create_ingredient() and plot the results using barh():

   thai_ingredient_df = create_ingredient_df(thai_df)
   thai_ingredient_df.head(10).plot.barh()
   

   

3. Repeat the process for Japanese cuisine:

   japanese_ingredient_df = create_ingredient_df(japanese_df)
   japanese_ingredient_df.head(10).plot.barh()
   

   

4. Do the same for Chinese cuisine:

   chinese_ingredient_df = create_ingredient_df(chinese_df)
   chinese_ingredient_df.head(10).plot.barh()
   

   

5. Plot the ingredients for Indian cuisine:

   indian_ingredient_df = create_ingredient_df(indian_df)
   indian_ingredient_df.head(10).plot.barh()
   

   

6. Finally, plot the ingredients for Korean cuisine:

   korean_ingredient_df = create_ingredient_df(korean_df)
   korean_ingredient_df.head(10).plot.barh()
   

   

7. Remove common ingredients that might cause confusion between cuisines using drop():

   Everyone loves rice, garlic, and ginger!

   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 the data is cleaned, use SMOTE - "Synthetic Minority Over-sampling Technique" - to balance it.

1. Use fit_resample() to generate new samples through interpolation.

   oversample = SMOTE()
   transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)
   

   Balancing your data improves classification results. For example, in binary classification, if most of your data belongs to one class, the model will predict that class more often simply because there's more data for it. Balancing the data reduces this bias.

2. Check the number of labels per ingredient:

   print(f'new label count: {transformed_label_df.value_counts()}')
   print(f'old label count: {df.cuisine.value_counts()}')
   

   The output looks like this:

   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 now clean, balanced, and ready for analysis!

3. Save the balanced data, including labels and features, into a new dataframe for export:

   transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')
   

4. Take one last look at the data using transformed_df.head() and transformed_df.info(). Save a copy of this data for use in future lessons:

   transformed_df.head()
   transformed_df.info()
   transformed_df.to_csv("../data/cleaned_cuisines.csv")
   

   The new CSV file is now available in the root data folder.

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🚀Challenge

This curriculum includes several interesting datasets. Explore the data folders to find datasets suitable for binary or multiclass classification. What questions could you ask of these datasets?

Post-lecture quiz

Review & Self Study

Learn more about SMOTE's API. What use cases is it best suited for? What problems does it address?

Assignment

Explore classification methods

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