import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

def standardize(x):
    mean_x = x.mean(axis=0)
    std_x = x.std(axis=0)
    return (x - mean_x)/std_x

boston = load_boston()
X = boston.data
y = boston.target
X_std = standardize(X)
x_0 = np.ones(X.shape[0]).reshape(-1, 1)
X_std = np.hstack([x_0, X_std])
y_std = standardize(y)
X_train, X_test, y_train, y_test = train_test_split(X_std, y_std, test_size=0.3, random_state=42)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)