Titanic-Survival-Prediction ataturhan21 · PARTIAL
A comprehensive solution for the Kaggle Titanic Challenge, featuring advanced data exploration, feature engineering, model training, and explainable AI techniques. Includes Logistic Regression, RandomForest, XGBoost, and Stacked Ensembles with SHAP and permutation importance for model interpretability.
github.com/ataturhan21/Titanic-Survival-Prediction · ★ 29 · Forks 0 · Size 1.8 MB
SUMMARY
Technologies 5
Scored 3
Observed 3
Practices 6
Evidence 6
Skips 2
COVERAGE
Analyzed 5 files · 11 commits · 0 API calls
TECHNOLOGIES & DEPTH
Markdown LANGUAGE Depth 70
1 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
NumPy LIBRARY Depth —
0 files · config only
RabbitMQ MESSAGE_BROKER Depth 41
1 files · CONFIGURATION
pandas LIBRARY Depth —
0 files · config only
PRACTICES
documentation · observedautomated_tests · absentcontinuous_integration · absentcontainerization · absentlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2024-10-05
Last commit 2024-11-20
Active months 2
Commits 11