normalize data using sklearn code example

Example 1: feature scaling in python

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
from sklearn.linear_model import Ridge
X_train, X_test, y_train, y_test = train_test_split(X_data, y_data,
                                                   random_state = 0)

X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Example 2: Scaling features to a range

# Scaling features to a range using MinMaxScaler

X_train = np.array([[ 1., -1.,  2.],
                    [ 2.,  0.,  0.],
                    [ 0.,  1., -1.]])

min_max_scaler = preprocessing.MinMaxScaler()
X_train_minmax = min_max_scaler.fit_transform(X_train)
X_train_minmax
# array([[0.5		, 0.		, 1.	    ],
#        [1.		, 0.5		, 0.33333333],
#        [0.		, 1.		, 0.		]])

X_test = np.array([[-3., -1.,  4.]])
X_test_minmax = min_max_scaler.transform(X_test)
X_test_minmax
# array([[-1.5		,	0.		, 	1.66666667]])

min_max_scaler.scale_
# array([0.5       , 0.5       , 0.33...])

min_max_scaler.min_
# array([0.       , 0.5       , 0.33...])