breast-cancer-wdbc-ml Taimisson · PARTIAL
End-to-end machine learning pipeline for breast cancer diagnosis (malignant vs. benign) using the Wisconsin WDBC dataset. Achieved 96.5% accuracy with Logistic Regression. Features: EDA, feature selection, model training, and interpretability analysis.
github.com/Taimisson/breast-cancer-wdbc-ml · ★ 3 · Forks 0 · Size 6.3 MB
SUMMARY
Technologies 6
Scored 4
Observed 4
Practices 6
Evidence 7
Skips 2
COVERAGE
Analyzed 25 files · 16 commits · 0 API calls
TECHNOLOGIES & DEPTH
Markdown LANGUAGE Depth 70
2 files · PRODUCTION
YAML 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 65
1 files · CONFIGURATION
pandas LIBRARY Depth —
0 files · config only
PRACTICES
documentation · observedautomated_tests · absentcontinuous_integration · absentcontainerization · absentlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2026-02-13
Last commit 2026-05-20
Active months 2
Commits 16