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, you will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
The recommended prerequisites for this project are:
The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.
GDE in Machine Learning | Intel Software Innovator
Sayak is a GDE in Machine Learning, an Intel Software Innovator, and a Data Science Educator. Full-stack data science and machine learning interpretability are the subjects he loves the most. He enjoys applying deep learning to solve real-world problems. Sayak also blogs about a wide range of topics in data science and machine learning. Check out his site to find out more about him and how to contact him.See More