This repository serves as a centralised hub for all things related to credit scoring. Welcome to the credit score repository! This project aims to explore and develop machine learning algorithms for credit scoring, with a focus on addressing the challenges posed by consumers with limited credit histories, often face difficulties in obtaining credit due to traditional scoring methods' limitations. This project seeks to leverage machine learning techniques to develop accurate and reliable credit scoring models specifically tailored for this demographic. Repository Structure:
- Data : Contains datasets used for training and evaluation
- Notebooks : VB Code/ R-studio for data preprocessing, exploratory analysis, model training and evaluation
- Scripts : Python scripts for preprocessing, modeling, and evaluation tasks.
- Models : Trained machine learning models for credit scoring
- Results : Visualisation, tables, and model performance
- References : Documentation and references to relevant literature and articles
- Documentation : Detailed instructions on using the code and reproducing research results
- License : Open-source license for the repository
- Contribution Guidelines : Guidelines for contributing to the project
To get started with this project, follow these steps:
- Clone the repository to your local machine
- Navigate to the relevant folders (e.g. Notebooks, Scripts)
- Explore the provided datasets and code.
- Run the notebooks or scripts to replicate the analysis and results.
Contribution to this project are welcome! If you'd like to contribute, please follow the contribution guidelines outlined in the repository.
This project is licensed under the MIT License.
For any question or inquiries, please contact Deepa Shukla Feel free to customize this templete to better fit your project's specific goals, requirements, and style. Let me know if you need further assistance!