Car Sales. Consider the data on used cars (ToyotaCorolla.csv) with 1436 records and details on 38 attributes, including Price, Age, KM, HP, and other specifications. The goal is to predict the price of a used Toyota Corolla based on its specifications.

• Fit a neural network model to the data. Use a single hidden layer with two nodes.

• Use predictors Age_08_04, KM, Fuel_Type, HP, Automatic, Doors, Quarterly_Tax, Mfr_Guarantee, Guarantee_Period, Airco, Automatic_airco, CD_Player, Powered_Windows, Sport_Model, and Tow_Bar.

• Remember to convert any categorical predictors to dummy variables using the Nominal to Numerical operator. Use the normalize parameter in the Neural Network operator to normalize the data to a [-1, 1] scale prior to model training. Record the RMS error for the training data and the holdout data. Repeat the process, changing the number of hidden layers and nodes to {single layer with five nodes} and {two layers, five nodes in each layer}.

a. What happens to the RMS error for the training data as the number of layers and nodes increases?

b. What happens to the RMS error for the holdout data?

c. Comment on the appropriate number of layers and nodes for this application.