Скачать или смотреть Data Transformation and Visualisation with Standard Scaler | Machine Learning

Data Transformation and Visualisation with Standard Scaler | Machine Learning

Standard Scaling - Part 64 - Data Transformation and Visualisation with Standard Scaler

Topic to be covered - How the data is transformed after applying Standard Scaler

Formula for the transformation - (Xi - mean(x)) / stdev(x)

Code Starts Here
==============

import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt
import seaborn as sns


np.random.seed(1)

df = pd.DataFrame({
'x1' : np.random.normal(0,2,10000),
'x2' : np.random.normal(-3,5,10000)})

scaler = preprocessing.StandardScaler()
scaled_df = scaler.fit_transform(df)

scaled_df = pd.DataFrame(scaled_df, columns = ['col1','col2'])

fig , (ob1, ob2) = plt.subplots(ncols = 2, figsize=(5,5))
ob1.set_title('Before Scaling')
sns.kdeplot(df['x1'],ax=ob1)
sns.kdeplot(df['x2'],ax=ob1)

ob2.set_title('After Standard Scaler')
sns.kdeplot(scaled_df['col1'],ax=ob2)
sns.kdeplot(scaled_df['col2'],ax=ob2)

plt.show()

plt.scatter(df['x1'],df['x2'], color = 'green')

plt.scatter(scaled_df['col1'],scaled_df['col2'], color = 'red')

sns.jointplot(df['x1'],df['x2'],color='yellow')
plt.show()


sns.jointplot(scaled_df['col1'],scaled_df['col2'],color='blue')
plt.show()

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