In this first problem, our goal is to use KNN to predict whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign.

We will consider a hypothetical customer with the following data:

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 $=1$

Credit Card $=1$

Before beginning the analysis, partition the data into training $(60\%)$ and validation $(40\%)$ sets. Then complete the following:

Perform a KNN 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?

Find a choice of k that balances between overfitting and ignoring the predictor information.

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

Classify the customer from Step B using the best k$.$