Data Description: The file Bank.xls 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.

Domain:Banking

Context:This case is about a bank (Thera Bank) whose management 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 campaigns with better target marketing to increase the success ratio with minimal budget.

Objective:The classification goal is to predict the likelihood of a liability customer buying personal loans.


ID : unique identifier
Personal Loan : did the customer accept the personal load offered (1=Yes, 0=No)
Age : customer’s age
Experience : number of years of profession experience
Income : annual income of the customer ($000)
Zip code: home address zip code
Family : family size of customer
CCAvg : average spending on credit cards per month ($000)
Education: education level (1) undergraduate, (2) graduate, (3) advanced/professional
Mortgage : value of house mortgage ($000)
Securities : does the customer have a securities account with the bank? (1=Yes, 0=No)
CDAccount : does the customer have a certificate of deposit with the bank? (1=Yes, 0=No)
Online : does the customer use Internet banking facilities (1=Yes, 0=No)
CreditCard : does the customer use a credit card issued by Universal Bank? (1=Yes, 0=No)