Can I please get help understanding the following work below:

Calculating Distance with Categorical Predictors. This question with a tiny datasetillustrates the calculation of Euclidean distance and the creation of binary dummies.The online education company Statistics.com segments its customers andprospects into three main categories: IT professionals (IT), statisticians (Stat) andother(Other). It also tracks, for each customer, the number of years since first

contact(years). Consider the following customers; information about whether theyhave taken a course or not (the outcome to be predicted) is included:

Customer 1: Stat, 1 year, did not take course

Customer 2: Other, 1.1 year, took course

1. Consider now the following new prospect:

Prospect 1: IT, 1 year

Using the above information on the two customers and one prospect, create one dataset for all three with the categorical predictor variable transformed into 2 binaries, and a similar dataset with the categorical predictor variable transformed into 3 binaries.

B.For each derived dataset, calculate the Euclidean distance between the prospect and each of the other two customers. (Note: while it is typical to normalize data fork-NN, this is not an iron-clad rule and you may proceed here without normalization.)

C.Usingk-NN withk= 1, classify the prospect as taking or not taking a course using each of the two derived datasets. Does it make a difference whether you use 2 or 3 dummies?