This repository is a collection of the following guided projects I completed on datacamp.com, focused on data science in the Applied Finance sector. If the .ipynb files have any rendering issues, kindly view the html output of the notebook files here: https://htmlpreview.github.io/
After a debt has been legally declared "uncollectable" by a bank, the account is considered to be "charged-off." But that doesn't mean the bank simply walks away from the debt. They still want to collect some of the money they are owed. In this project, I used regression discontinuity , an intuitive and useful analysis method in any situation of threshold assignment and other statistical methods to assess a situation where a bank assigned delinquent customers to different debt recovery strategies based on the expected amount the bank believed it would recover from the customer.
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, I built an automatic credit card approval predictor using machine learning techniques, just like the real banks do.
The dataset (located inside the folder called datasets) used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.