Splitting the data

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3,random_state=42)

Scaling the data

We can first fit the scaler (with the training data only) and the fit the training and testing data, or we can directy fit the scaler and transform the training data in one step, and then transform the test data. I show an example of each method below.

Standard Scaler

from sklearn.preprocessing import Standard Scaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

Min-Max Scaler

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)