A machine learning based credit scorecard for lending risk analysis.
The purpose of this repository is to create a credit scoring model for a potential lending company. Historical data is provided. The business use case of this application aims to solve the following objectives:
- Achieve an overall default rate of total portfolio to be below 2.5%, and to provide recommendations on the optimal credit score cutoff rate.
- Create a credit score for each individual, which is validated, and to provide guidance on the next steps.
- Create deciles by credit score and provide risk and default levels by deciles (and cumulative).
- Provide confidence for credit scores and default rates by bin.
- Any libraries needed before running this program is provided in
requirements.txt
- cd to the directory where requirements.txt is located.
- Activate your virtualenv.
- Run: pip install -r requirements.txt in your shell.
- Modify path in cell 1 of
modelling.ipynb
to root of credit-scoring directory.
- The program can be run through the jupyter notebooks.
- 0.3 (TODO)
- Create python scripts for inference
- 0.2 (TODO)
- 0.1
- Initial Release
This project is licensed under the MIT License - see the LICENSE.md file for details.