Create app.py

app.py

@@ -0,0 +1,22 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error
+diabetes = datasets.load_diabetes()
+diabetes_X = diabetes.data[:, np.newaxis,2]
+diabetes_X_train = diabetes_X[:-30]
+diabetes_X_test = diabetes_X[-30:]
+
+diabetes_y_train = diabetes.target[:-30]
+diabetes_y_test = diabetes.target[-30:]
+model = linear_model.LinearRegression()
+model.fit(diabetes_X_train,diabetes_y_train)
+diabetes_y_predicted = model.predict(diabetes_X_test)
+#(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename', 'data_module'])
+
+print("mean squared error is:", mean_squared_error(diabetes_y_test,diabetes_y_predicted))
+print("weight:",model.coef_)
+print("Intercept:", model.intercept_)
+plt.scatter(diabetes_X_test,diabetes_y_test)
+plt.plot(diabetes_X_test,diabetes_y_predicted)
+plt.show()