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GitHub / Ndaruga / Fraud-Detection-in-credit-cards-using-anormaly-detection

This project involves identifying unusual patterns or behaviors that may indicate fraudulent activity in credit cards. Anomaly detection algorithms work by learning patterns from historical data and then detecting deviations from those patterns in new data. Two algorithms have been utilized. i.e Autoencoders & Angle-based Outlier Detection (ABOD)

JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ndaruga%2FFraud-Detection-in-credit-cards-using-anormaly-detection

Stars: 1
Forks: 1
Open issues: 0

License: None
Language: Jupyter Notebook
Size: 104 MB
Dependencies parsed at: Pending

Created at: about 2 years ago
Updated at: 11 months ago
Pushed at: about 1 year ago
Last synced at: 11 months ago

Topics: angle-based-outlier-detection, autoencoders

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