Anomaly detection is where we search for unexpected values in a given dataset. An anomaly is a system behavior deviation or data value deviation from the standard value. There are other names for anomalies, such as outliers, errors, deviations, and exceptions. They can occur in data that's of diverse nature and structure as a result of technical failures, accidents, deliberate hacks, and more.
There are many methods and algorithms we can use to search for anomalies in various types of data. These methods use different approaches to solve the same problem. There are unsupervised, supervised, and semi-supervised algorithms. However, in practice, unsupervised methods are the most popular. The unsupervised anomaly detection technique detects anomalies in unlabeled test datasets, under the assumption that most of the dataset is normal. It does this by searching...
