Bank Loan Modeling

Objective:

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

Context:

The bank has a growing customer base. The bank wants to increase borrowers (asset customers) base to bring in more loan business and earn more through the interest on loans. So , the bank wants to convert the liability based 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. The department wants to build a model that will help them identify the potential customers who have a higher probability of purchasing the loan. This will increase the success ratio while at the same time reduce the cost of the campaign.

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 the bank?

Steps and tasks:

1. Importing the datasets and libraries, check datatype, statistical summary, shape, null values etc
2. Checking if we need to clean the data for any of the variables
3. EDA: Study the data distribution in each attribute and target variable.

• Number of unique in each column
• Number of people with zero mortgage
• Number of people with zero credit card spending per month
• Value counts of all categorical columns
• Univariate and Bivariate analysis

4. Applying necessary transformations for the feature variables
5. Normalising the data and splitting the data into training and test set in the ratio of 70:30 respectively
6. Using Logistic Regression model to predict the likelihood of a customer buying personal loans.
7. Displaying all the metrics related for evaluating the model performance
8. Building various other classification algorithms and comparing their performance

• Support Vector Machine
• K-Nearest Neighbors
• Naive Bayes Classifier