a$.$ Consider the following customer:

Age $=40,$ Experience $=10,$ Income $=84,$ Family $=2,$ CCAvg $=2,$ Education$\_1$

$=0,$ Education$\_2=1,$ Education$\_3=0,$ Mortgage $=0,$ Securities Account $=0,$

CD Account $=0,$ Online $=1,$ and Credit Card $=1.$ Perform a k$-$NN classification

with all predictors except ID and ZIP code using k $=1.$ Remember to transform

categorical predictors with more than two categories into dummy variables first.

Specify the success class as $1($loan acceptance$),$ and use the default cutoff value of

$0\mathrm{.}5.$ How would this customer be classified?

b$.$ What is a choice of k that balances between overfitting and ignoring the predictor

information?

c$.$ Show the confusion matrix for the validation data that results from using the best k$.$

d$.$ Consider the following customer: Age $=40,$ Experience $=10,$ Income $=84,$

Family $=2,$ CCAvg $=2,$ Education$\_1=0,$ 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$.$

e$.$ Repartition the data, this time into training, validation, and test sets $(50\%$ : $30\%$ :

$20\%).$ Apply the k$-$NN method with the k chosen above. Compare the confusion

matrix of the test set with that of the training and validation sets. Comment on the

differences and their reason.