Data Mining in R Program

Write a fully executed R-Markdown program and submit a pdf file solving and answering questions listed below under Problems at the end of chapter 7. For clarity, make sure to give an appropriate title to each section.

Problem 7.2: Personal Loan Acceptance (a, b, c, d)

Problem 7.3: Predicting Housing Median Prices (a, b)

7.2 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 ban

k.

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 depart[1]ment 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.

7.3 Predicting Housing Median Prices. The file BostonHousing.csv contains infor[1]mation on over 500 census tracts in Boston, where for each tract multiple variables

are recorded. The last column (CAT.MEDV) was derived from MEDV, such that it

obtains the value 1 if MEDV > 30 and 0 otherwise. Consider the goal of predicting

the median value (MEDV) of a tract, given the information in the first 12 columns.

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

a. Perform a k-NN prediction with all 12 predictors (ignore the CAT.MEDV col[1]umn), trying values of k from 1 to 5. Make sure to normalize the data, and choose

function knn() from package class rather than package FNN. To make sure R is

using the class package (when both packages are loaded), use class::knn(). What

is the best k? What does it mean?

b. Predict the MEDV for a tract with the following information, using the best k: