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NiharMandahas
/
RF_Customer_Fraud

Tabular Classification
Scikit-learn
Joblib
fraud-detection
random-forest
Model card Files Files and versions
xet
Community

Instructions to use NiharMandahas/RF_Customer_Fraud with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Scikit-learn

    How to use NiharMandahas/RF_Customer_Fraud with Scikit-learn:

    from huggingface_hub import hf_hub_download
    import joblib
    model = joblib.load(
    	hf_hub_download("NiharMandahas/RF_Customer_Fraud", "sklearn_model.joblib")
    )
    # only load pickle files from sources you trust
    # read more about it here https://skops.readthedocs.io/en/stable/persistence.html
  • Notebooks
  • Google Colab
  • Kaggle
RF_Customer_Fraud
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  • 1 contributor
History: 12 commits
NiharMandahas's picture
NiharMandahas
Delete model_files
0d33af4 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    1.1 kB
    Upload README.md with huggingface_hub over 1 year ago
  • app.py
    756 Bytes
    Update app.py over 1 year ago
  • fraud_detection_pipeline.py
    1.92 kB
    Create fraud_detection_pipeline.py over 1 year ago
  • random_forest_model.joblib
    11.4 MB
    xet
    Upload random_forest_model.joblib with huggingface_hub over 1 year ago
  • requirements.txt
    47 Bytes
    Update requirements.txt over 1 year ago
  • rf_scaler.joblib

    Detected Pickle imports (6)

    • "joblib.numpy_pickle.NumpyArrayWrapper",
    • "numpy.ndarray",
    • "numpy.core.multiarray._reconstruct",
    • "_codecs.encode",
    • "numpy.dtype",
    • "sklearn.preprocessing._data.StandardScaler"

    How to fix it?

    1.15 kB
    xet
    Upload rf_scaler.joblib with huggingface_hub over 1 year ago