Credit risk assessment helps financial institutions decide whether to extend loans by predicting the likelihood of default. This project uses machine learning algorithms like Logistic Regression, Random Forest, and SVM to predict credit risk based on customer demographic and financial data.
active 2024-09-30 → 2024-09-30 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2024-09-30 to 2024-09-30. Pushes: 7 total, peak 7 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 |
|---|---|---|---|---|
| Eshaaaaa | 7 | 7 | 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.