Partition the data into training (60%] and validation (4U%} sets. E" Consider the following customer: Age = 411}, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education_1 = U, Education_2 = 1, Education_3 = 0, Mortgage = 0, Securities Account = '3, CD Account = U, lUnline = 1, and Credit Card = 1. Perform a kNN classification with all predictors except ID and ZIP code using [c = 1. Remember to transform categorical predictors with more than two categories into diuruny variables first. Specify the success class as 1 (loan acceptance), and use the default cutoff value of 0.5. How would this customer be classied? . What is a choice of 1C. that balances between overfitting and ignoring the predictor information? Show the confusion matrix for the validation data that results from using the best k. Consider the following customer: Age = 40, Experience = 10, Income = 84, Family = 2, CCAvg = 2, Education_1 = U, Education_2 = 1, Education_3 = 0', Mortgage = 0, Securities Account = 0, CD Account = 0, Online = 1 and Credit Card = 1. Classify the customer using the best k. Repartition the data, this time into training, validation, and test sets (50% : 30% : 20%}. Apply the kNN method with the k chosen above. Compare the confusion matrix of the