andrewnguyen07 / credit-risk-management

Credit Risk Management Project

Credit Score Cards are one of the common risk control methods in the financial industry which uses personal information and transactional records to identify and evaluate the creditworthiness of existing and potential customers. There are a number of different use cases leveraging this measure such as loan management, credit card approval, credit limit extension, etc.

That said, this project's applicability varies depending on the problem a financial institution is facing. The core engine which makes this project usable is the processing and transformation of input data to engender high predictability of the output that best addresses the issues.

A blog post is coming under way to further accompany this repository, which will be updated once ready.

Dataset

The dataset used in this project is shared publicly via Kaggle, which includes 2 sub-sets: personal information and transactional records. We will utilise and merge both files into one dataset to make the analysis more insightful and actionable.

Requirements

• pandas
• numpy
• matplotlib
• seaborn
• sklearn
• category_encoders
• imblearn
• xgboost
• lightgbm

Agenda

1. Explanatory Data Analysis (EDA) and Feature Engineering
2. Feature Scaling and Selection (+ Imbalanced Data Handling)
3. Machine Learning Modelling