This project aims to develop a machine learning model that can analyze customer data and predict whether a customer is likely to churn. By identifying potential churners in advance, organizations can take proactive steps to improve customer retention and reduce revenue loss.
active 2026-03-21 → 2026-03-21 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2026-03-21 to 2026-03-21. Pushes: 3 total, peak 3 in a day. Pull requests: 2 total, peak 2 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Stars, PRs, issues and forks are under-captured in the later part of this window. GH Archive progressively stopped capturing non-push events during 2026 — −95% or worse by the end of the window. Every series here except Pushes fades for that reason, so a decline above reflects the archive, not this repository. Pushes stay reliable throughout, so read them, and the contributor counts derived from them, as the real signal. Data health has the measurements.
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| chandanc5525 | 5 | 3 | 2 | 0 |
| github-actions[bot] | 1 | 0 | 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 — 1 stars here means stars gained during the window, not the repo's star count.