# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 2)
X_poly = poly_reg.fit_transform(X)
poly_reg.fit(X_poly, y)
lin_reg_2 = LinearRegression()
lin_reg_2.fit(X_poly, y)
lin_reg_2.score(X_poly,y)

#Backward el
X = np.append(arr = np.ones((120, 1)).astype(int), values = X_poly , axis = 1)

from statsmodels.regression.linear_model import OLS as sm
X_opt = X[:,:]
regressor_ols = sm(endog = y, exog = X_opt).fit()
regressor_ols.summary()