The data include customer demographic information $($age$,$ income, etc.$),$ the customer's relationship with the bank $($mortgage$,$ securities account, etc.$),$ and the customer response to the last personal loan campaign $($Personal Loan$).$ Among these $5000$ customers,

campaign.

only $480(=9\mathrm{.}6\%)$ accepted the personal loan that was offered to them in the earlier

Partition the data into training $(60\%)$ and validation $(40\%)$ sets.

$$Consider the following customer:

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

$2,$ 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 $=10.$ How would this customer be classified? $($Note: This analysis

may take a few minutes.$)$

$$What is a choice of k that balances between overfitting and ignoring the predictor information?

$$Show the classification 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 $=2,$ 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 k$-$NN method with the k chosen above. Compare the classification matrix of the test set with that of