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fraud-detection-models

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nafisalawalidris / Three-Model-Deep-Learning-Fraud-Detection-System

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Deep learning fraud detection system using MLP, Autoencoder, and VAE for imbalanced credit card data. Built with PyTorch, it includes SMOTE, RobustScaler preprocessing, FastAPI REST API for real-time predictions, and an interactive dashboard. Features EDA, ROC-AUC/PR-AUC evaluation, and unit tests.

machine-learning deep-learning pytorch multilayer-perceptron-network smote fraud-detection anomaly-detection variational-autoencoder credit-card-fraud-detection fastapi autoencoder-neural-network robustscaler fraud-detection-models
  • Updated Aug 29, 2025
  • Jupyter Notebook

KarthikMahalingam8881 / Optimizing-Credit-Card-Fraud-Detection

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This project converts a Jupyter-based machine learning model into a modular, cloud-ready data engineering pipeline using Python, AWS S3, and PostgreSQL. It enables automated data ingestion, transformation, and loading of credit card transaction data for downstream analysis or modeling.

credit-card-fraud-detection credit-card-fraud-detection-using-machine-learning credit-card-analysis fraud-detection-models credit-fraud-analysis machine-learning-for-fraud deep-learning-fraud-detection fraud-classification-models fraud-transaction-analysis fraud-detection-python imbalanced-dataset-handling
  • Updated Jun 9, 2025
  • Jupyter Notebook

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