agconti/kaggle-titanic
A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
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agconti/kaggle-titanic has +0 stars this period . Velocity data will be available after more historical data is collected.
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| Metric | kaggle-titanic | rl-mpc-locomotion | libxsmm | llms-interview-questions |
|---|---|---|---|---|
| Stars | 949 | 949 | 949 | 948 |
| Forks | 677 | 92 | 202 | 111 |
| Weekly Growth | +0 | +0 | +0 | +0 |
| Language | Jupyter Notebook | Python | C | N/A |
| Sources | 1 | 1 | 1 | 1 |
| License | Apache-2.0 | MIT | BSD-3-Clause | N/A |
Capability Radar vs rl-mpc-locomotion
Last code push 711 days ago.
Fork-to-star ratio: 71.3%. Active community forking and contributing.
Issue data not yet available.
No measurable growth in the current period (first-day cold start expected).
Licensed under Apache-2.0. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.