The objective is to construct a model that can be used to determine which type of customer will be most likely to sign the offer for a personal loan, based on their specific connection to the bank over a variety of features that are given in the dataset. The classification goal is to identify the probability of a customer purchasing a personal loan. I will be utilizing supervised machine learning concepts to make a prediction on a model, and according to me, logistic regression will be the most effective solution for this problem.
Available attributes in the data set are:
ID : Customer ID
Age : Customer's age in completed years
Experience : years of professional experience
Income : Annual income of the customer
ZIP Code : Home Address ZIP code.
Family : Family size of the customer
CCAvg : Avg. spending on credit cards per month
Education : Education Level.1: Undergrad; 2: Graduate; 3: Advanced/Professional
Mortgage : Value of house mortgage if any.
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
Potentially relevant attributes are:
Personal Loan
Age
Income
CCAvg
Mortgage