The fully prepared training and testing data is reloaded below and assigned to the following variables:

• X_train_final
• X_test_final
• y_train_final
• y_test_final

For the last question, you will HELP WITH another OLS model using 1 Principal Component.

• Instantiate an instance of PCA with the following configuration and assign topca_one:

PCA(n_components=1, random_state=24)

• Usingpca_one, fit and transformX_train_finaland assign the output toX_train_pca.
• Usingpca_one, transformX_test_finaland assign output toX_test_pca(Be careful not to refit on testing data!)
• HELP WITH anOLSregression model using statsmodels:
• Prepend a column of ones toX_train_pcaandX_test_pcafor the intercept term (statsmodels.api.add_constant(df))
• Fit an OLS model usingX_train_pcaandy_train_final. Assign the model topca_model
• Usingpca_model, create predictions forX_test_pcaand assign topca_test_preds

In[]:

X_train_final = read_training_data(final=True) y_train_final = read_y_train(final=True) X_test_final = read_testing_data(final=True) y_test_final = read_y_test(final=True) 

In[]:

### GRADED import statsmodels.api as sm ### YOUR SOLUTION HERE pca_one = None X_train_pca = None X_test_pca = None pca_model = None pca_preds = None