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PythonMachine LearningXGBoostAnomaly Detection
Fraud Detection Analysis
Built a machine learning pipeline to detect fraudulent financial transactions using anomaly detection and classification models.
Critical Analysis
Beyond identifying fraud patterns, our team recognized the dataset's synthetic nature, an unusually high 32% fraud rate compared to the 1-2% seen in real-world banking, and flagged this limitation rather than overstating the findings' real-world applicability.
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