Predict yearly customer spending with Python. Utilizes Pandas for data handling, Seaborn for visual insights, and Scikit-learn for machine learning. Analyze session length, app usage, website engagement, and predict spending patterns. Ideal for exploring customer behavior and implementing predictive models in business analytics.
active 2024-06-26 → 2024-06-27 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 2 days from 2024-06-26 to 2024-06-27. Pushes: 2 total, peak 2 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: 1 total, peak 1 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 |
|---|---|---|---|---|
| Anshtyagi1729 | 2 | 2 | 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 — 1 stars here means stars gained during the window, not the repo's star count.