https://github.com/snehexcel/upi_frauddetection_ml

Built a simple machine learning model with Linear Regression to predict fraudulent UPI transactions. Used SMOTE to balance the data by generating synthetic fraud samples. The model learns patterns from transaction features like amount, time, and merchant category to identify fraud risk. This provides a clear and easy baseline for fraud detection.
https://github.com/snehexcel/upi_frauddetection_ml

Last synced: 10 months ago
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Built a simple machine learning model with Linear Regression to predict fraudulent UPI transactions. Used SMOTE to balance the data by generating synthetic fraud samples. The model learns patterns from transaction features like amount, time, and merchant category to identify fraud risk. This provides a clear and easy baseline for fraud detection.

• Host: GitHub
• URL: https://github.com/snehexcel/upi_frauddetection_ml
• Owner: snehexcel
• Created: 2025-09-06T12:39:20.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2025-09-06T12:40:38.000Z (10 months ago)
• Last Synced: 2025-09-06T14:36:56.999Z (10 months ago)
• Language: Jupyter Notebook
• Size: 5.11 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Upi_FraudDetection_ML

Built a simple machine learning model with Linear Regression to predict fraudulent UPI transactions. Used SMOTE to balance the data by generating synthetic fraud samples. The model learns patterns from transaction features like amount, time, and merchant category to identify fraud risk. This provides a clear and easy baseline for fraud detection.