aws-solutions-library-samples/fraud-detection-using-machine-learning
Setup end to end demo architecture for predicting fraud events with Machine Learning using Amazon SageMaker
Implements both supervised (XGBoost) and unsupervised (RandomCutForest) models to handle labeled and unlabeled fraud datasets, with built-in handling for class imbalance through upsampling and scale position weighting. Deploys trained models to SageMaker real-time endpoints exposed via API Gateway and Lambda, enabling REST API integration with business systems, while Kinesis Firehose streams predictions to S3 and QuickSight for monitoring and visualization.
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