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minimum-covariance
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Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.
visualizationneural-networkstatistical-analysisoutlierscnn-kerasanomaly-detectionzscoreknn-classificationlocal-outlier-factorone-class-svmiforest-modelpyodautoencoder-neural-networkinliersanomoly-scoreminimum-covariance
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Dec 19, 2021 - Jupyter Notebook
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