def print_evaluation_metrics(trained_model,trained_model_name,X_test,y_test):
print ('--------- For Model : ', trained_model_name ,' ---------\n')
predicted_values = trained_model.predict(X_test)
#print ("Predicted Values : ", predicted_values)
print ("Mean Absolute Error : ", metrics.mean_absolute_error(y_test,predicted_values))
print ("Mean Squared Error : ", metrics.mean_squared_error(y_test,predicted_values))
print ("R2 Score : ", metrics.r2_score(y_test,predicted_values))
print ("---------------------------------------\n")
actual_values = y_test[30000:30050]
plt.plot(predicted_values[30000:30050], color='green', label='Predicted Values')
plt.plot(actual_values, color='red', label='Actual Values')
plt.xlabel('Week Number')
plt.ylabel('Weekly Sales')
plt.legend(loc='upper right')
plt.title('Comparison of Predicted and Actual Weekly Sales for ', trained_model_name)
plt.show()