Outlier detection is a critical process in data analysis and machine learning aimed at identifying data points that deviate significantly from the rest of the dataset. These anomalies, or outliers, can arise due to various factors, including measurement errors, data entry mistakes, or rare events. Detecting outliers is essential for ensuring data quality, as they can negatively impact the performance and accuracy of machine learning models. Various statistical and machine learning techniques, such as Z-score, IQR (Interquartile Range), and clustering-based methods, can be employed for outlier detection. By identifying and handling outliers appropriately, data scientists can improve model robustness, enhance interpretability, and obtain more reliable insights from their data analyses.