This Python program uses a Random Forest algorithm to analyze how customer bike-buying decisions are influenced by marital status, gender, education, occupation, and homeownership. Developed as part of a Machine Learning and Nural Computing course, it provides insights into factors affecting purchasing behavior.
active 2024-08-30 → 2024-08-30 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2024-08-30 to 2024-08-30. Pushes: 7 total, peak 7 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 1 total, peak 1 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 |
|---|---|---|---|---|
| ImeCMR | 8 | 7 | 0 | 1 |
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.