Description: Outlier handling refers to the methods and processes used to manage data that significantly deviates from expected behavior in a dataset. These values, known as outliers, can arise for various reasons, such as measurement errors, natural variations in the data, or unusual experimental conditions. Identifying and treating these values is crucial in data analysis, as they can distort the results of analyses and predictive models. There are various techniques for handling outliers, including removing these data points, transforming them, or using robust algorithms that minimize their impact. The relevance of this process lies in its ability to improve the accuracy and validity of analytical models, ensuring that the conclusions drawn from the data are representative and reliable. In summary, outlier handling is an essential component of data analysis, aimed at ensuring the integrity and quality of the results obtained.
