In today’s world of digital transactions, detecting fraudulent activities is a critical task for safeguarding financial systems. I recently completed a project where I built a machine learning model to detect fraudulent credit card transactions using Python and a real-world dataset.
active 2025-01-11 → 2025-01-11 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2025-01-11 to 2025-01-11. Pushes: 2 total, peak 2 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 |
|---|---|---|---|---|
| Tanuja-0431 | 2 | 2 | 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.