year, took course

a. 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 attribute transformed into two binaries and a similar dataset with the categorical predictor attribute transformed into three binaries. (Hint: Use Create ExampleSet operator in RapidMiner, and enter the data with dummy coded attributes for course categories; also add an id attribute and then set it to id role.)

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 for k-NN, this is not an iron-clad rule, and you may proceed here without normalization.)

c. Using k-NN with k = 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 two or three dummies?