The title of the project is, Predicting Credit Card Approvals
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. The aim of this project is to build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
Found in the notebook
- Data Sourcing: Web scraping or any other data sourcing method.
- Data Cleaning and Prep: Data Cleaning, preparation and basic statistics reporting
- Modeling: Base Model, Model Comparison, Hyper-parameter Tuning and monitoring with experiment management
- Model Deployment : Deploy on the web or mobile. You can leverage Google Colab/Streamlit/Huggyface where possible.
- Requirements.txt: A file for all dependecies required