By leveraging a probabilistic graphical model (PGM), the aim is to predict the likelihood of an employee leaving the company based on a variety of demographic, job-related, and behavioral factors. The model will capture direct influences and include them in a binary logistic regression, predicting whether an employee is likely to resign.
active 2025-03-26 → 2025-05-28 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 64 days from 2025-03-26 to 2025-05-28. Pushes: 44 total, peak 23 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
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 |
|---|---|---|---|---|
| Paulagranlund | 29 | 29 | 0 | 0 |
| annemollelsborg | 14 | 14 | 0 | 0 |
| sofiekaulberg | 1 | 1 | 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.