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Polynomial Regression with scikit-learn

Polynomial regression step-by-step

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Rearrange the code lines below in the correct order to fit the polynomial regression on array X, make predictions, and calculate the error.

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y_pred = model.predict(X_test)

rmse = mean_squared_error(y_test, y_pred, squared = False)

model = LinearRegression()

transformed_features = poly.fit_transform(np.array(X).reshape(-1, 1))

X_train, X_test, y_train, y_test = train_test_split(transformed_features, y, train_size=0.75, random_state=42)

model.fit(X_train, y_train)

poly = PolynomialFeatures(degree= 4 , include_bias= True)

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