Introduction$)$ The goal is to whether a new customer will accept a loan offer. This will serve as the basis for the design of a new campaign.

The file UniversalBank.xlsx contains data on $5000$ customers. 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, only $480(=9\mathrm{.}6\%)$ accepted the personal loan that was offered to them in the earlier campaign.

Partition the data into training $(60\%)$ and validation $(40\%)$ sets. Specify the success class as $1($loan acceptance$)$ and use the default cutoff value of $0\mathrm{.}5.$

$1)$ Using R perform a neutral net classification with all predictors except ID and ZIP code. Remember to transform categorical predictors with more than two categories into dummy variables first and scale numerical predictor variables to a $01.$

$2)$ Using R perform a deeper decision tree classification and prune with all predictors except ID and ZIP code.

$3)$ Based on the neutral net confusion matrix results versus the deeper decision tree confusion matrix results, identify which model method is best.

$4)$ 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 $=1.$ How would this customer be classified using neutral net or decision tree based on which was identified as performing best?

Note$)$ I want the actual R codes to be explained to solve this problem.