Outlier Detection

Outlier detection involves identifying data points that significantly differ from the majority of data collected. These unusual points may indicate errors, rare events, or unique insights. For example, in finance, a sudden large expense might be an outlier signaling fraud or a special purchase. Detecting outliers helps improve data quality, identify risks, or uncover important anomalies. Techniques vary from simple statistical methods to advanced algorithms, but their main goal is to flag data that doesn’t fit the usual pattern, allowing for better analysis and decision-making.