1. Import Dataset from above link. 2. Rescaling: Normalised the dataset using MinMaxScaler class 3. Standardizing Data (transform them into a standard Gaussian distribution with a mean of 0 and a standard deviation of 1)

 from pandas import *

from numpy import *

from sklearn import preprocessing

import scipy.stats as s

df=read_csv("winequality-red.csv")

print(df)

print("Rescaling Data")

print("\n\n Data scaled between 0 and 1")

data_scaler=preprocessing.MinMaxScaler(feature_range=(0,1))

data_scaled=data_scaler.fit_transform(df)

print("..........................")

print(data_scaled.round(2))

print("\n Standardizing Data ")

arr=array(df)

print(arr)

print("\n Initial mean : ",s.tmean(arr).round(2))

print("\n Initial Standard Deviation : ",round(arr.std(),2))

x_scaled=preprocessing.scale(arr)

x_scaled.mean(axis=0)

x_scaled.std(axis=0)

print("\n Standardizing data : \n",x_scaled.round(2))

print("\n Scaled Mean : ",s.tmean(x_scaled).round(2))

print("\n Scaled Standard Deviation : ",round(x_scaled.std(),2))