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