For many machine learning algorithms, it's good practice to scale the variables from 0 to 1. This is also called feature normalization. Let's apply the scaling transformation to achieve this:

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

After we scale the data, it is ready to be used as input to the different classifiers that we will present in the subsequent sections.