polynomial regression on boston housing data set.py

from sklearn.preprocessing import PolynomialFeatures

def create_polynomial_regression_model(degree):
  "Creates a polynomial regression model for the given degree"
  
  poly_features = PolynomialFeatures(degree=degree)
  
  # transforms the existing features to higher degree features.
  X_train_poly = poly_features.fit_transform(X_train)
  
  # fit the transformed features to Linear Regression
  poly_model = LinearRegression()
  poly_model.fit(X_train_poly, Y_train)
  
  # predicting on training data-set
  y_train_predicted = poly_model.predict(X_train_poly)
  
  # predicting on test data-set
  y_test_predict = poly_model.predict(poly_features.fit_transform(X_test))
  
  # evaluating the model on training dataset
  rmse_train = np.sqrt(mean_squared_error(Y_train, y_train_predicted))
  r2_train = r2_score(Y_train, y_train_predicted)
  
  # evaluating the model on test dataset
  rmse_test = np.sqrt(mean_squared_error(Y_test, y_test_predict))
  r2_test = r2_score(Y_test, y_test_predict)
  
  print("The model performance for the training set")
  print("-------------------------------------------")
  print("RMSE of training set is {}".format(rmse_train))
  print("R2 score of training set is {}".format(r2_train))
  
  print("\n")
  
  print("The model performance for the test set")
  print("-------------------------------------------")
  print("RMSE of test set is {}".format(rmse_test))
  print("R2 score of test set is {}".format(r2_test))