Scaling in Machine Learning

# Importing libraries
import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns 
% matplotlib inline
matplotlib.style.use('fivethirtyeight')

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def plot_comparison(x):
    scaler = preprocessing.RobustScaler()
    robust_df = scaler.fit_transform(x)
    robust_df = pd.DataFrame(robust_df, columns =['x1', 'x2'])

    scaler = preprocessing.StandardScaler()
    standard_df = scaler.fit_transform(x)
    standard_df = pd.DataFrame(standard_df, columns =['x1', 'x2'])

    scaler = preprocessing.MinMaxScaler()
    minmax_df = scaler.fit_transform(x)
    minmax_df = pd.DataFrame(minmax_df, columns =['x1', 'x2'])

    fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols = 4, figsize =(20, 5))
    ax1.set_title('Before Scaling')

    sns.kdeplot(x['x1'], ax = ax1, color ='r')
    sns.kdeplot(x['x2'], ax = ax1, color ='b')
    ax2.set_title('After Robust Scaling')

    sns.kdeplot(robust_df['x1'], ax = ax2, color ='red')
    sns.kdeplot(robust_df['x2'], ax = ax2, color ='blue')
    ax3.set_title('After Standard Scaling')

    sns.kdeplot(standard_df['x1'], ax = ax3, color ='black')
    sns.kdeplot(standard_df['x2'], ax = ax3, color ='g')
    ax4.set_title('After Min-Max Scaling')

    sns.kdeplot(minmax_df['x1'], ax = ax4, color ='black')
    sns.kdeplot(minmax_df['x2'], ax = ax4, color ='g')
    plt.show()

x = pd.DataFrame({
    # Distribution with higher outliers
    'x1': np.concatenate([np.random.normal(10, 2, 1000), np.random.normal(50, 2, 25)]),
    # Distribution with lower outliers
    'x2': np.concatenate([np.random.normal(100, 2, 1000), np.random.normal(60, 2, 25)]),
})

plot_comparison(x)

x = pd.DataFrame({
    # Distribution with lower outliers
    'x1': np.concatenate([np.random.normal(20, 2, 1000), np.random.normal(1, 2, 25)]),
    # Distribution with higher outliers
    'x2': np.concatenate([np.random.normal(30, 2, 1000), np.random.normal(50, 2, 25)]),
})

plot_comparison(x)

x = pd.DataFrame({
    # Distribution with lower outliers
    'x1': np.concatenate([np.random.normal(10, 2, 1000), np.random.normal(0, 2, 25)]),
    # Distribution with higher outliers
    'x2': np.concatenate([np.random.normal(100, 5, 1000), np.random.normal(110, 5, 25)]),
})

plot_comparison(x)

x = pd.DataFrame({
    # Distribution with lower outliers
    'x1': np.concatenate([np.random.normal(10, 2, 1000), np.random.normal(-20, 1, 25)]),
    # Distribution with higher outliers
    'x2': np.concatenate([np.random.normal(100, 5, 1000), np.random.normal(130, 1, 25)]),
})

plot_comparison(x)

x = pd.DataFrame({
    # Distribution with lower outliers
    'x1': np.concatenate([np.random.normal(10, 2, 1000), np.random.normal(-20, 20, 25)]),
    # Distribution with higher outliers
    'x2': np.concatenate([np.random.normal(100, 5, 1000), np.random.normal(130, 50, 25)]),
})

plot_comparison(x)