Skip to content

Latest commit

 

History

History
40 lines (35 loc) · 1.45 KB

README.md

File metadata and controls

40 lines (35 loc) · 1.45 KB

Supervised-Learning

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

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.

Attribute Information:  ID : Customer ID  Age : Customer's age in completed years  Experience : #years of professional experience  Income : Annual income of the customer ($000)  ZIP Code : Home Address ZIP code.  Family : Family size of the customer  CCAvg : Avg. spending on credit cards per month ($000)  Education : Education Level.

  1. Undergrad
  2. Graduate
  3. Advanced/Professional  Mortgage : Value of house mortgage if any. ($000) Personal Loan : Did this customer accept the personal loan offered in the last campaign?  Securities Account : Does the customer have a securities account with the bank?  CD Account : Does the customer have a certificate of deposit (CD) account with the bank?  Online : Does the customer use internet banking facilities?  Credit card : Does the customer use a credit card issued by UniversalBank?