Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.
active 2023-08-15 → 2026-05-02 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 992 days from 2023-08-15 to 2026-05-02. Pushes: 1 total, peak 1 in a day. Pull requests: 2 total, peak 1 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 274 total, peak 4 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 |
|---|---|---|---|---|
| ashishpatel26 | 2 | 1 | 1 | 0 |
| solegalli | 1 | 0 | 1 | 0 |
Recent activity
Latest issues, pull requests and releases
- Pull request#4ashishpatel262025-06-29 09:32
- Pull request#4solegalli2025-06-28 15:04
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 274 stars here means stars gained during the window, not the repo's star count.