In real-world scenarios, the attributes present in data will be in a varying pattern. So, rescaling of the characteristics to a common scale gives benefit to algorithms to process the data efficiently.
We can rescale the data using Scikit-learn. The code for rescaling the data using MinMaxScaler is as follows:
#Rescaling data
import pandas
import scipy
import numpy
from sklearn.preprocessing import MinMaxScaler
names = ['Abhi', 'Piyush', 'Pranay', 'Sourav', 'Sid', 'Mike', 'pedi', 'Jack', 'Tim']
Dataframe = pandas.read_csv(url, names=names)
Array = dataframe.values
# Splitting the array into input and output
X = array[:,0:8]
Y = array[:,8]
Scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# Summarizing the modified data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])
