Handbook of Anomaly Detection: With Python Outlier Detection — (1) Introduction
Anomaly detection is the detection of any rare events that deviate significantly from the majority of the data. Those rare events do not conform to a well-defined behavior. They are also called Outliers, noises, novelties, or exceptions. Rare events can detrimentally impact the business operation and result in a significant loss. Companies must detect them quickly and accurately to reduce and prevent future losses. Anomaly detection, therefore, becomes an important topic in data science. Data professionals have been directly or indirectly involved in the detection and prevention of fraud or risk mitigation.
Various types of modeling algorithms have been proposed for anomaly detection. It will be very helpful to present anomaly detection algorithms from easy to complex. It will be ideal to explain some selected algorithms and demonstrate them with code examples. Such a handbook hopefully will assist data professionals in detecting anomalies. This is the motivation for this handbook.
