Various Studies and Experts in Machine Learning / building Predictive Models suggest that about two-thirds of the effort needs to be dedicated to Data Understanding and Data Pre-processing Stages. The purpose of this blog is to cover the two techniques i.e. Anomaly Detection and Outlier Detection, that are used during the Data Understanding and Data Pre-processing stages.
Anomaly Detection is also a task on its own. Anomaly detection finds extensive use in a wide variety of applications such as fraud detection for credit cards, insurance or health care, intrusion detection for cyber-security, fault detection in safety critical systems, and military surveillance for enemy activities