Preparing the data (20%)

XX--Move "Model" to column1. One way is to use the dplyr library from the tidyverse package.

Zz Let the model names be your rownames (not to be used as data, but will be the name of each row). See James textbook, page 55 for some guidance.

XX Ensure that the data is complete before doing the analysis. Look for "NA" entries, especially in the "Rear.seat.room" and "Luggage.room" columns. You may choose to replace "NA" with 0.

XX Look at the spelling of "Chrysler" as you go down the column. Fix the error.

"Cylinders" has a non-numeric term. Make an appropriate decision.

If necessary, replace qualitative data with quantitative data.

For binary data, e.g. Origin: USA/non-USA can be coded 1 or 0. Do you need two columns, or one? You can use the dummy( ) function from the dummies package.

For nominal or ordinal data, egDriveTrain, you can use 3 sets of binary data columns: Front : 1 or 0, Rear: 1 or 0, and 4WD: 1 or 0. You can use the dummy_cols( ) function in the fastDummies package.

Remove the original columns of categorical/binary data if dummies have been substituted.

You can remove columns of data that you think are unnecessary, but you must justify that decision.

Principal Components Analysis (25%)

Perform PCA on your Cars93 Data.

Display and report on your findings (this is totally up to you).

Use your graphics, and explain how you choose 3 possible cars to suggest to each of the following customers:

(a) A student wants a cheap, fuel-efficient car, and is not concerned of its origin.

(b) A mom with four young children can only drive automatic and loves US cars.

(c) A middle-aged executive wants a sporty, non-US vehicle. For him, price is not a factor.

(d) A consulate wants a luxury midsize sedan for its personnel. They are not willing to purchase US-made vehicles because of a political situation.

(e) A family of 6-footers who want a midsize car, not a van.