PROJECT DESCRIPTION

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this project, an automatic credit card approval predictor is made by making use of machine learning techniques, just like the real banks do.

The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.

PROJECT DETAILS

1. Credit card applications
2. Inspecting the applications
3. Splitting the dataset into train and test sets
4. Handling the missing values (part i)
5. Handling the missing values (part ii)
6. Handling the missing values (part iii)
7. Preprocessing the data (part i)
8. Preprocessing the data (part ii)
9. Fitting a logistic regression model to the train set
10. Making predictions and evaluating performance
11. Grid searching and making the model perform better
12. Finding the best performing model