This project demonstrates deploying a machine learning model as a REST API using Flask and Docker. It trains a Random Forest Classifier on the Iris dataset, serves predictions via a /predict endpoint, and containerizes the application for portability. Easy-to-use, real-time predictions!
active 2025-01-12 → 2025-01-13 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 2 days from 2025-01-12 to 2025-01-13. Pushes: 5 total, peak 3 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 |
|---|---|---|---|---|
| Azacus1 | 5 | 5 | 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.