Capstone Project: Default Rate Prediction

This repository contains the core components of the project focused on predicting loan defaulters using historical lending data.

Repository Contents

• Notebook/ — Main analysis and modeling workflow
• docs/ — Project documentation outlining methodology, assumptions, and decisions
• slides/ — Presentation materials summarizing key findings
• App/ — Contains the training data and model specifications.

Technologies Used

• Python (Pandas, NumPy, Scikit-learn, Seaborn)
• Google Colab
• Streamlit

Project Highlights

• End-to-end data science workflow
• Modular and reproducible code
• Deployed solution demonstrating practical application

Deployment

A live version of the project is available here.

Additional Notes

This repository is intended to showcase structure, methodology, and deployment practices. For full details, refer to the documentation and notebook.

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