[ML] Feature_Scaling 예제

dataset

Data_Feature_Scaling.xlsx

0.01MB

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from sklearn import preprocessing

data_set = pd.read_excel('/content/Data_Feature_Scaling.xlsx')
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)

""" MIN MAX SCALER """
min_max_scaler = preprocessing.MinMaxScaler(feature_range=(0,1))

#Scaled feature
min_max_scaler.fit(x)
x_after_min_max_scaler = min_max_scaler.transform(x)

print ("\nAfter min max Scaling : \n",x_after_min_max_scaler)

""" Standardisation """
Standardisation = preprocessing.StandardScaler()

#Scaled feature
Standardisation.fit(x)
x_after_Standardisation = Standardisation.transform(x)

print ("\nAfter Standardisation : \n",x_after_Standardisation)

https://github.com/erica00j/machinelearning/blob/main/Feature_Scaling.ipynb