Description: Outlier analysis refers to the examination of data points that significantly deviate from the expected behavior within a dataset. These values, known as outliers, can arise for various reasons, such as measurement errors, natural variations in the data, or rare events. Identifying and understanding these outliers is crucial, as they can influence the results of statistical analyses and predictive models, distorting data interpretation. In the context of data science and data mining, outlier analysis allows analysts to detect unusual patterns that may indicate problems or opportunities. For example, in fraud detection, outliers can signal suspicious transactions that require further investigation. Additionally, analyzing these data points can help improve the quality of predictive models, ensuring they are based on more representative information. In summary, outlier analysis is an essential tool for understanding the complexity of data and its impact on informed decision-making.
