Uses content-based filtering to suggest events based on user preferences (category & budget). Leverages TF-IDF and cosine similarity on a dataset of events with details like name, location, description, and price. Provides personalized recommendations and allows user exploration within budget constraints. Built with Python and scikit-learn.
active 2024-11-05 → 2024-11-05 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2024-11-05 to 2024-11-05. Pushes: 3 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 |
|---|---|---|---|---|
| VasaviVarnika | 3 | 3 | 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.