# Lasso Model lasso = LassoCV(alphas = np.logspace(-4, 0, 100), normalize = True, cv = 5, max_iter = 100000) lasso.fit(x_train.values, y_train.values) mse = np.mean(lasso.mse_path_, axis = 1) # Plot fig, ax = plt.subplots(figsize=(12,6)); ax.plot(lasso.alphas_, mse, color='red'); ax.plot(lasso.alpha_, np.min(mse), marker='o', color='blue', markersize=8); ax.set_xlabel('alpha'); ax.set_ylabel('MSE for Train Set'); lasso.predicted = lasso.predict(college_test[features].values) # MSE MSE = np.mean((college_test.Apps.values-lasso.predicted)**2) print(MSE) #1865826.9804312338 print(pd.Series(data = np.hstack([lasso.intercept_,lasso.coef_]), index=['Intercept'] + features)) # part e # PCR Model scores = [] num_components = np.arange(1,len(features)+1) num_samples = x_train.shape[0]