Commit a3e15ff
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Complete 6-model unified ML pipeline for NASA exoplanet detection
Implements production-ready machine learning pipeline with consistent
data processing, comprehensive testing, and automated benchmarking
across 6 algorithms (XGBoost, Random Forest, MLP, Logistic Regression,
SVM, CNN1D).
Key achievements:
- Unified data pipeline (600/200/200 split) with TDD (119 tests, 100% pass)
- CNN1D with GPU optimization and Path object handling fix
- Complete benchmark: Random Forest leads (ROC-AUC 0.7468)
- Automated artifact generation (confusion matrices, metrics, visualizations)
- Cross-platform batch execution (bash/bat/Python)
- Google Colab optimization support
This completes the transformation from separate model scripts to a
cohesive, test-driven, production-ready ML pipeline with comprehensive
documentation and reproducible results.1 parent 5fa4855 commit a3e15ff
52 files changed
Lines changed: 16000 additions & 193 deletions
File tree
- .github/workflows
- configs
- docs
- notebooks
- scripts
- src
- models
- cnn1d
- logistic_regression
- mlp
- random_forest
- svm
- xgboost
- tests
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