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ML | Feature Scaling – Part 2

  • Difficulty Level : Medium
  • Last Updated : 05 Jul, 2021

Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units. If feature scaling is not done, then a machine learning algorithm tends to weigh greater values, higher and consider smaller values as the lower values, regardless of the unit of the values.

Example: If an algorithm is not using the feature scaling method then it can consider the value 3000 meters to be greater than 5 km but that’s actually not true and in this case, the algorithm will give wrong predictions. So, we use Feature Scaling to bring all values to the same magnitudes and thus, tackle this issue.

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Techniques to perform Feature Scaling
Consider the two most important ones:



  • Min-Max Normalization: This technique re-scales a feature or observation value with distribution value between 0 and 1.
    X_{\text {new }}=\frac{X_{i}-\min (X)}{\max (x)-\min (X)}
  • Standardization: It is a very effective technique which re-scales a feature value so that it has distribution with 0 mean value and variance equals to 1.
    X_{\text {new }}=\frac{X_{i}-X_{\text {mean }}}{\text { Standard Deviation }}

Download the dataset:
Go to the link and download Data_for_Feature_Scaling.csv


Code: Python code explaining the working of Feature Scaling on the data




# Python code explaining How to
# perform Feature Scaling
   
""" PART 1
    Importing Libraries """
   
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
  
# Sklearn library 
from sklearn import preprocessing
  
""" PART 2
    Importing Data """
   
data_set = pd.read_csv('C:\\Users\\dell\\Desktop\\Data_for_Feature_Scaling.csv')
data_set.head()
  
# here Features - Age and Salary columns 
# are taken using slicing
# to handle values with varying magnitude
x = data_set.iloc[:, 1:3].values
print ("\nOriginal data values : \n",  x)
  
  
""" PART 4
    Handling the missing values """
  
from sklearn import preprocessing
  
""" MIN MAX SCALER """
  
min_max_scaler = preprocessing.MinMaxScaler(feature_range =(0, 1))
  
# Scaled feature
x_after_min_max_scaler = min_max_scaler.fit_transform(x)
  
print ("\nAfter min max Scaling : \n", x_after_min_max_scaler)
  
  
""" Standardisation """
  
Standardisation = preprocessing.StandardScaler()
  
# Scaled feature
x_after_Standardisation = Standardisation.fit_transform(x)
  
print ("\nAfter Standardisation : \n", x_after_Standardisation)

Output :

   Country  Age  Salary  Purchased
0   France   44   72000          0
1    Spain   27   48000          1
2  Germany   30   54000          0
3    Spain   38   61000          0
4  Germany   40    1000          1

Original data values : 
 [[   44 72000]
 [   27 48000]
 [   30 54000]
 [   38 61000]
 [   40  1000]
 [   35 58000]
 [   78 52000]
 [   48 79000]
 [   50 83000]
 [   37 67000]]

After min max Scaling : 
 [[ 0.33333333  0.86585366]
 [ 0.          0.57317073]
 [ 0.05882353  0.64634146]
 [ 0.21568627  0.73170732]
 [ 0.25490196  0.        ]
 [ 0.15686275  0.69512195]
 [ 1.          0.62195122]
 [ 0.41176471  0.95121951]
 [ 0.45098039  1.        ]
 [ 0.19607843  0.80487805]]

After Standardisation : 
 [[ 0.09536935  0.66527061]
 [-1.15176827 -0.43586695]
 [-0.93168516 -0.16058256]
 [-0.34479687  0.16058256]
 [-0.1980748  -2.59226136]
 [-0.56487998  0.02294037]
 [ 2.58964459 -0.25234403]
 [ 0.38881349  0.98643574]
 [ 0.53553557  1.16995867]
 [-0.41815791  0.43586695]]



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