Project uses ML models (XGBoost, Regression, RandomForest, Neural Networks) to predict bank customer churn. XGBoost led with 86.73% accuracy. Utilized Tableau for visualization, hosted online via Docker, Neon, Flask.
active 2024-01-26 → 2025-02-03 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 375 days from 2024-01-26 to 2025-02-03. Pushes: 90 total, peak 21 in a day. Pull requests: 8 total, peak 6 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 |
|---|---|---|---|---|
| Talieh-Sh | 75 | 67 | 8 | 0 |
| michaelz-id | 13 | 13 | 0 | 0 |
| ronaspen | 8 | 8 | 0 | 0 |
| Sim0304 | 2 | 2 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
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.