Anomaly Detection

Anomaly detection is a process used in data analysis and machine learning to identify patterns or events that deviate significantly from expected behavior within a dataset. These deviations, known as anomalies or outliers, may indicate critical incidents, such as fraud, network security breaches, operational failures, or other unusual occurrences that warrant further investigation.Anomaly detection algorithms work by establishing a model of what constitutes normal behavior and then flagging observations that do not conform to this model. Techniques used for anomaly detection can be broadly categorized into statistical methods, machine learning techniques (such as supervised and unsupervised learning), and distance-based methods.The goal of anomaly detection is to improve decision-making by allowing organizations to proactively address potential risks or opportunities that may not be evident through standard analysis methods. Effective anomaly detection can enhance efficiency, security, and reliability across various applications, including finance, healthcare, and manufacturing.