Create a machine learning application using Streamlit to predict customer churn in a telecommunications company. This tool leverages predictive analytics to identify potential churners among subscribers. With Streamlit's interactive interface, stakeholders can easily explore churn prediction results, enabling proactive measures to retain customers
active 2024-02-13 → 2024-03-14 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 31 days from 2024-02-13 to 2024-03-14. Pushes: 72 total, peak 23 in a day. Pull requests: 20 total, peak 8 in a day. Issues: 0 total, peak 0 in a day. Comments: 1 total, peak 1 in a day. Stars: 1 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
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- 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 |
|---|---|---|---|---|
| Code-str8 | 93 | 72 | 20 | 1 |
Recent activity
Latest issues, pull requests and releases
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 1 stars here means stars gained during the window, not the repo's star count.