withsivram/Fraud-detection-and-Insights-Using-Machine-Learning
View on GitHub ↗Related repositories →This project uses advanced machine learning models (XGBoost, LightGBM, CatBoost) to detect fraudulent transactions. It includes data preprocessing, feature engineering, model explainability (SHAP), and deployment-ready pipelines for robust fraud detection and analysis.
active 2024-12-10 → 2024-12-10 (UTC)
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Line chart, 1 days from 2024-12-10 to 2024-12-10. Pushes: 0 total, peak 0 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
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