This project applies machine learning to predict customer spending scores based on demographic features like age, gender, and annual income. By analyzing these patterns, we aim to enhance customer segmentation, optimize marketing strategies, and support decision-making for targeted campaigns.
active 2024-11-10 → 2024-11-21 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 12 days from 2024-11-10 to 2024-11-21. Pushes: 8 total, peak 5 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 |
|---|---|---|---|---|
| Hardik-Girdhar | 8 | 8 | 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.