Banks receive a lot of applications for issuance of credit cards. Many of them are rejected for many reasons, like high-loan balances, low-income levels, or too many inquiries on an individual’s credit report. Manually analyzing these applications is error-prone and a time consuming process. This task can be automated with the power of machine learning. In this project, we will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
The task is to analyze and build a predictive model that can accurately determine whether a credit card application should be approved or rejected based on various applicant attributes and historical credit data. The goal is to develop a model that can assist financial institutions in making informed decisions while minimizing the risk of default and maximizing profitability.
They have given a problem to identify the customers segments which are eligible for Credit Card approval, so that they can specifically target these customers.
Note –
● DAYS_BIRTH ---> Count backwards from current day (0), -1 means yesterday.
● DAYS_EMPLOYED ---> Count backwards from current day (0). If positive, it means the person currently unemployed.
● MONTHS_BALANCE ---> The month of the extracted data is the starting point, backwards, 0 is the current month, -1 is the previous month, and so on.
● STATUS ---> 0: 1-29 days past due 1: 30-59 days past due 2: 60-89 days overdue 3: 90-119 days overdue 4: 120-149 days overdue 5: Overdue or bad debts, writeoffs for more than 150 days C: paid off that month X: No loan for the month
● Understand data by performing exploratory data analysis ● Training and building classification algorithm to predict if a customer will be approved with credit card or not ● Understand feature importance and improve the model ● Understand various model performance metrics and measure the performance of each model