Screen Time Analysis is the task of analyzing and creating a report on which applications and websites are used by the user for how much time. Screen Time Analysis tells how much time we spend on what kind of applications and websites while using our device. In this project, I'll be using python with pandas and numpy along with a dataset.
active 2024-01-07 → 2024-06-13 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 159 days from 2024-01-07 to 2024-06-13. Pushes: 7 total, peak 6 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 |
|---|---|---|---|---|
| HimanshuR321 | 7 | 7 | 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.