The project analyzed LinkedIn's job market by ethically scraping data using Python (Selenium, Beautiful Soup) and cleaning it with Pandas. It identified job market trends through Power BI and DAX, with insights presented via an interactive dashboard. Collaboration with cross-functional teams ensured a thorough analysis.
active 2024-07-16 → 2024-11-05 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 113 days from 2024-07-16 to 2024-11-05. Pushes: 24 total, peak 11 in a day. Pull requests: 2 total, peak 2 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 |
|---|---|---|---|---|
| anjanicoder | 20 | 18 | 2 | 0 |
| Salil-Singh-01 | 2 | 2 | 0 | 0 |
| Jayadavv | 1 | 1 | 0 | 0 |
| aam1rkhan | 1 | 1 | 0 | 0 |
| tauheed7080 | 1 | 1 | 0 | 0 |
| devanjali2117 | 1 | 1 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
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.