Predicting Prices of Used Cars. The file ToyotaCorolla.csv contains data on used cars (Toyota Corolla) on sale during late summer of 2004 in the Netherlands. It has 1436 records containing details on 38 attributes, including Price, Age, Kilometers, HP, and other specifications. The goal is to predict the price of a used Toyota Corolla based on its specifications. (The example in Section 6. 3 is a subset of this dataset.) Split the data into training (50%), validation (30%), and holdout (20%) datasets. Run a multiple linear regression with the target attribute Price and 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.

a. What appear to be the three or four most important carspecifications for predicting the car’s price?

b. Using metrics you consider useful, assess the performance of the model in predicting prices