implementation of lin reg model algorithm

Kaggle/LinearRegression.py

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+#%%
+''' this is a implementation of the Linear regression model
+from scratch without using any library '''
+import numpy as np
+X = 2* np.random.rand(100,1)
+X
+
+#%%
+#the dependent equation i.e Y's value depends on Y
+Y = 4 + 3 * X + np.random.randn(100, 1)
+Y
+#%%
+#creates 100 X 1 shape matrix with value 1
+np.ones((100,1))
+#%%
+X_b = np.c_[np.ones((100, 1)), X]
+# add x0 = 1 to each instance
+X_b
+
+#%%
+theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(Y)
+theta_best
+
+#%%
+X_new = np.array([[0], [2]])
+X_new_b = np.c_[np.ones((2, 1)), X_new] # add x0 = 1 to each instance
+y_predict = X_new_b.dot(theta_best)
+y_predict
+
+#%%
+import matplotlib.pyplot as plt
+plt.plot(X_new, y_predict, "r-")
+plt.plot(X, Y, "g.")
+plt.axis([0, 2, 0, 15])
+plt.show()
+
+#%%
+#The equivalent implementation of the above Algorithm
+from sklearn.linear_model import LinearRegression
+lin_reg = LinearRegression()
+lin_reg.fit(X, Y)
+lin_reg.intercept_, lin_reg.coef_
+lin_reg.predict(X_new)