Data Preprocessing Questions
Outlier detection is the process of identifying and handling data points that deviate significantly from the normal or expected patterns in a dataset. Outliers are data points that are either extremely high or low compared to the majority of the data. Detecting outliers is important in data preprocessing as they can have a significant impact on the analysis and modeling process, leading to inaccurate results. Outlier detection techniques involve statistical methods, such as z-score or modified z-score, or machine learning algorithms, such as clustering or isolation forest, to identify and handle outliers appropriately.