Supervised-Learning

Objective

To build a classification model to predict the likelihood of a liability customer buying personal loans.

Data Description

The dataset 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.

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?

Learning Outcomes

 Exploratory Data Analysis
 Preparing the data to train a model
 Training and making predictions using a classification model
 Model evaluation

Steps to be followed

1. Read the column description and ensure you understand each attribute well
2. Study the data distribution in each attribute, share your findings
3. Get the target column distribution. Your comments
4. Split the data into training and test set in the ratio of 70:30 respectively
5. Use different classification models (Logistic, K-NN and Naïve Bayes) to predict the likelihood of a liability customer buying personal loans
6. Print the confusion matrix for all the above models
7. Give your reasoning on which is the best model in this case and why it performs better?