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nafiul-araf / Anomaly-Detection

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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.

visualization neural-network statistical-analysis outliers cnn-keras anomaly-detection zscore knn-classification local-outlier-factor one-class-svm iforest-model pyod autoencoder-neural-network inliers anomoly-score minimum-covariance
  • Updated Dec 19, 2021
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