ECON 3200: Homework 1

library(tidyverse)
library(openintro)

The file UniversalBank.rds 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.

Data Description:

Variable	Description
ID	Customer ID
Age	Customer’s age in completed years
Experience	# years of professional experience
Income	Annual income of the customer ($000)
ZIPCode	Home Address ZIP code
Family	Family size of the customer
CCAvg	Avg. spending on credit cards per month ($000)
Education_1	Education Level = 1 if Undergrad; 0 otherwise
Education_2	Education Level = 1 if Graduate; 0 otherwise
Education_3	Education Level = 1 if Advanced/Professional; 0 otherwise
Mortgage	Value of house mortgage if any. ($000)
PersonalLoan	Did this customer accept the personal loan offered in the last campaign?
SecuritiesAccount	Does the customer have a securities account with the bank?
CDAccount	Does the customer have a certificate of deposit (CD) account with the bank?
Online	Does the customer use internet banking facilities?
CreditCard	Does the customer use a credit card issued by UniversalBank?

Question 1

Read and preprocess the data: a. Read the data file and assign into an R object. b. Drop ID and ZIPcode from the dataset. c. Convert binary categorical variables into factor variables. d. Handle missing values, if there is any. e. Normalize all numerical variables.

Answer to Question 1

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Question 2

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

Answer to Question 2

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Question 3

Perform a k-NN classification with all predictors except ID and ZIP code using:
a. k = 1. b. k = 2. c. k = 3. d. k = 4.

For each k=1,2,3,4, compute accuracy based on test dataset. Whick k is the best?

Answer to Question 3

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Question 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, SecuritiesAccount = 0, CDAccount = 0, Online = 1, and CreditCard = 1.

Perform a k-NN classification with all predictors except ID and ZIP code using th best k. How would this customer be classified?

Answer to Question 4

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