#diabetes = np.loadtxt('RegressionData.txt',dtype=np.float,delimiter=",")# Use only one featurediabetes_X=diabetes.data[:,np.newaxis]diabetes_X_temp=diabetes_X[:,:,2]# Split the data into training/testing setsdiabetes_X_train=diabetes_X_temp[:-20]diabetes_X_test=diabetes_X_temp[-20:]# Split the targets into training/testing setsdiabetes_y_train=diabetes.target[:-20]diabetes_y_test=diabetes.target[-20:]# Create linear regression objectregr=linear_model.LinearRegression()# Train the model using the training setsregr.fit(diabetes_X_train,diabetes_y_train)# The coefficientsprint('Coefficients: \n',regr.coef_)# The mean square errorprint("Residual sum of squares: %.2f"%np.mean((regr.predict(diabetes_X_test)-diabetes_y_test)**2))# Explained variance score: 1 is perfect predictionprint('Variance score: %.2f'%regr.score(diabetes_X_test,diabetes_y_test))# Plot outputsplt.scatter(diabetes_X_test,diabetes_y_test,color='black')plt.plot(diabetes_X_test,regr.predict(diabetes_X_test),color='blue',linewidth=3)plt.xticks(())plt.yticks(())plt.show()