Scaling, Normalization, Min Max.
n_informative=5, n_redundant=0, random_state=1) from sklearn.preprocessing import StandardScaler steps = [('scaler', StandardScaler()),('knn', KNeighborsClassifier())] pipeline = Pipeline(steps) X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.2, random_state=21) knn_scaled =