Using the logistic data set from https://www.kaggle.com/datasets/ziya07/smart-logistics-supply-chain-dataset . I was able to build a xgboost model that is able to accurately predict a test set of 20% of the full data using the other 80% to train the model.
active 2025-03-12 → 2025-03-12 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2025-03-12 to 2025-03-12. Pushes: 0 total, peak 0 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
Nobody pushed, opened or commented here in the loaded window — this repo's activity is stars and forks only.
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.