7. Polynomial Regression.md

poly = PolynomialFeatures (degree = 2)
X_poly = poly.fit_transform(X_final)

X_train,X_test,y_train,y_test = train_test_split(X_poly,y_final, test_size = 0.33, random_state = 0)

#standard scaler (fit transform on train, fit only on test)
sc = StandardScaler()
X_train = sc.fit_transform(X_train.astype(np.float))
X_test= sc.transform(X_test.astype(np.float))

#fit model
poly_lr = LinearRegression().fit(X_train,y_train)

y_train_pred = poly_lr.predict(X_train)
y_test_pred = poly_lr.predict(X_test)