Can someone help me solve this using the Rstudio program

Universal Bank is a relatively young bank growing rapidly in terms of overall customer

acquisition. The majority of these customers are liability customers (depositors) with varying

sizes of relationship with the bank. The customer base of asset customers (borrowers) is quite

small, and the bank is interested in expanding this base rapidly to bring in more loan business.

In particular, it wants to explore ways of converting its liability customers to personal loan

customers (while retaining them as depositors). A campaign that the bank ran last year for

liability customers showed a healthy conversion rate of over 9% success. This has encouraged

the retail marketing department to devise smarter campaigns with better target marketing. The

goal is to use k-NN to predict 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.csv 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 (= 9.6%) accepted the personal loan that was offered to them in the earlier campaign.

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

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 0.5. 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.