What is Normalization?
This method also known as Min-Max Scaling involves the rescaling of values into a common scale between a range of [0,1] or [-1,1]. It is best suited when there are no outliers. In Python we use a transformer from the Scikit-Learn package called MinMaxScaler for Normalization.
Firstly we will import our dataset as follows.
PYTHON
1import pandas as pd
2import numpy as np
3
4df = pd.read_csv("/content/winequality-red.csv")
5
6# Choosing 3 features for our analysis
7wine_df = df.loc[:,['quality','alcohol','density']]Next using MinMaxScaler we will perform Normalization on our data.
PYTHON
1from sklearn.preprocessing import MinMaxScaler
2scaling = MinMaxScaler()
3
4scaling.fit_transform(wine_df[['quality','alcohol']])