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README.md

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+---
+tags:
+- employee-turnover
+- xgboost
+- mlflow
+- classification
+library_name: scikit-learn
+---
+
+# Employee Turnover Prediction Model
+
+Modèle XGBoost pour prédire le turnover des employés.
+
+## Utilisation
+
+```python
+import mlflow
+model = mlflow.sklearn.load_model("hf://ASI-Engineer/employee-turnover-model")
+predictions = model.predict(X)
+```

model/MLmodel

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+flavors:
+  python_function:
+    env:
+      conda: conda.yaml
+      virtualenv: python_env.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    predict_fn: predict
+    python_version: 3.12.3
+  sklearn:
+    code: null
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 1.6.1
+mlflow_version: 3.8.0
+model_id: null
+model_size_bytes: 220543
+model_uuid: 2d0516757a5e44b59a76f41795abfe7c
+prompts: null
+utc_time_created: '2025-12-25 21:40:40.507658'

model/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.12.3
+- pip<=25.3
+- pip:
+  - mlflow==3.8.0
+  - cloudpickle==3.1.2
+  - imbalanced-learn==0.13.0
+  - numpy==2.4.0
+  - pandas==2.3.3
+  - pytest==9.0.1
+  - scikit-learn==1.6.1
+  - scipy==1.16.3
+  - xgboost==2.1.4
+name: mlflow-env

model/model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e51ada1b28e1a028f547cde968f04b83d2bb18dd8613f8dfbdb7ad7a279f4c2
+size 220543

model/python_env.yaml

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+python: 3.12.3
+build_dependencies:
+- pip==25.3
+- setuptools
+- wheel
+dependencies:
+- -r requirements.txt

model/requirements.txt

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+mlflow==3.8.0
+cloudpickle==3.1.2
+imbalanced-learn==0.13.0
+numpy==2.4.0
+pandas==2.3.3
+pytest==9.0.1
+scikit-learn==1.6.1
+scipy==1.16.3
+xgboost==2.1.4