Use theCars93data set from the built-in package "MASS" available in R.

You are to analyse the data using unsupervised learning.

1) Preparing the data

****Any packages and functions referred to below are just suggestions. You can use different methods/packages/functions to achieve your result.**** YOU DO NOT HAVE TO DO IT LIKE THIS, ANY METHOD IN R IS FINE

Preparing the data

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

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.

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.

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, eg DriveTrain, 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.