LinearRegression1.py

from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression 
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt
import numpy as np


diabetes_X , diabetes_y = load_diabetes(return_X_y=True)

diabetes_X=diabetes_X[:,np.newaxis,2]

diabetes_X_train = diabetes_X[:-20]
diabetes_X_test = diabetes_X[-20:]

diabetes_y_train = diabetes_y[:-20]
diabetes_y_test = diabetes_y[-20:]

lin_reg= LinearRegression()
lin_reg.fit(diabetes_X_train,diabetes_y_train)

diabetes_y_pred = lin_reg.predict(diabetes_X_test)
mse= mean_squared_error(diabetes_y_test,diabetes_y_pred)
print("mse: " , mse)
r2=r2_score(diabetes_y_test,diabetes_y_pred)
print("r2 score: " , r2)

plt.scatter(diabetes_X_test,diabetes_y_test,color="black")
plt.plot(diabetes_X_test,diabetes_y_pred,color="blue")
plt.show()