model = LinearRegression()
params = {'polynomialfeatures__degree': np.arange(10)}
model1 = GridSearchCV(model, params, cv=10, scoring='r2')
model1.fit(X, Y)
print("Best Hyper Parameters:n",model1.best_params_)

poly_reg = PolynomialFeatures(degree = 3)
        X_poly = poly_reg.fit_transform(X)
        cv_results = model_selection.cross_val_score(model, X_poly, Y.ravel(), cv=kfold, scoring='r2')