min max scaler sklearn
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit_transform(X_train)
scaler.transform(X_test)
The Frenchy
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit_transform(X_train)
scaler.transform(X_test)# mean and standard deviation normalisation
normalized_df=(df-df.mean())/df.std()
# min max scaling
normalized_df=(df-df.min())/(df.max()-df.min())