Personal Loan Acceptance. Universal Bank is a relatively young bank growing rapidly in terms of overall customer acquisi

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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 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. PROBLEMS 185 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.