This project predicts whether a smartphone user will download an app after clicking a mobile ad using Random Forest. It includes data preprocessing, feature engineering, model building, and evaluation. Key insights include feature importance and model accuracy, with visualizations to support analysis.
active 2024-11-03 → 2024-11-08 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 6 days from 2024-11-03 to 2024-11-08. Pushes: 16 total, peak 7 in a day. Pull requests: 29 total, peak 14 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 |
|---|---|---|---|---|
| nikhil9066 | 45 | 16 | 29 | 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.