Anomaly detection is a step in data mining that identifies data points, events, and/or observations that deviate from a dataset's normal behavior. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior.
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What's your experience with Anomaly Detection?
Any best practices to share, or pitfalls to avoid?
What is the most interesting dataset you have ever worked with?
Please note: The dataset that is used in this presentation is the publicly available data presented by The Washington Post.