Personal Loan Acceptance. 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 customers 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. Remember to transform categorical predictors with more than two categories into dummy variables first. Also, use only the following predictors: Age, Experience, Income, Family, CCAvg, Education, Mortgage, Securities Account, CD Account, Online and Credit Card. For kNN, use library class, which automatically handles categorical predictors, rather than FNN.

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

b. Find the best k, based on accuracy of the confusion matrix for k = 1 to 10.

please include R code and screenshots please