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
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Updated
Dec 19, 2021 - Jupyter Notebook