This repository offers machine learning solutions for predicting loan defaults using XGBoost and Random Forest models. It focuses on enhancing model transparency and interpretability through Explainable AI techniques, addressing the needs of data scientists, stakeholders, and regulators in the financial industry.
active 2023-12-06 → 2023-12-06 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2023-12-06 to 2023-12-06. Pushes: 6 total, peak 6 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| ivarrtheboneless | 6 | 6 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.