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Upload folder using huggingface_hub
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Upload folder using huggingface_hub
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feat: update ML pipeline with preprocessing module and string-based inputs
- Replace integer-encoded fields (genre, heure_supplementaires, frequence_deplacement) with human-readable Literal/String types in schema and DB models - Add departement and niveau_hierarchique_poste fields to schema and DB models - Extract feature engineering from predict route into ml_model/preprocessing.py - Update best_pipeline.pkl to include preprocessing stepsf67b60a
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feat: add FastAPI REST API with HR churn prediction endpoint
- Create app/main.py with FastAPI app and lifespan model loading - Add Pydantic schemas for input validation (24 fields) and structured output (prediction, label, probabilite) - Implement /health, /model/info and /predict endpoints - Add ml_model/loader.py to load pipeline and custom threshold from pkl - Apply feature engineering before inference: derive stabilite_management, flag_surcharge_et_deplacement, ratio_promotion_anciennete, satisfaction_globale, satisfaction_min, log_revenu; drop 3 raw columns - Classify with custom threshold (predict_proba >= threshold) - Update requirements.txt with fastapi, uvicorn, scikit-learn, pandase59e621 -
feat: add serialized ML pipeline
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init: project structure and base configuration
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