import numpy as np

np.random.seed(42)
m = 100
X = 2 * np.random.rand(m,1)
y = 4 + 3 * X + np.random.randn(m, 1)

import matplotlib.pyplot as plt
plt.plot(X, y, "b.")
plt.show()

from sklearn.preprocessing import add_dummy_feature

X_b = add_dummy_feature(X)
theta_best = np.linalg.inv(X_b.T @ X_b)