Tools for credit risk scoring models development and portfolio management
Project description
Risk Modeling Tools
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Table of Contents
About The Project
There are many great libraries providing credit risk analysis tools on github.
However, I couldn't find one that can cover all my needs. Hope you'll find it useful for your projects.
Here are the tools available in the library:
- Binning tool for both categorical and numerical features
- Feature selection tool for linear regression
- Basic optimization algorithms for credit portfolio management
Built With
Here are the libraries used for the project.
Getting Started
Prerequisites
For using the library you need the following things
- python 3
- install pip or clone the git repository
Installation
Bellow is the installation command using pip
pip install risk-modeling-tools
Usage
The library may be used for scoring models developing and credit risk analysis
Roadmap
- Add Changelog
- Add Additional Templates w/ Examples
See the open issues for a full list of proposed features (and known issues).
Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE.txt
for more information.
Contact
Mikhail Gritskikh - @m_gritskikh - m.gritskikh@gmail.com
Project Link: https://github.com/mgrts/risk_modeling_tools
Acknowledgments
Some useful links for better understanding
Change Log
0.0.1 (12/05/2021)
- First Release
Project details
Release history Release notifications | RSS feed
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