Outlier detection is an important field of study and has a wide range of applications. Fraud detection, anomalous data, and intrusion detection are some examples.
Outliers are data points that deviate significantly from the normal distribution or projected trends within a dataset in the context of data analysis. These data points can introduce noise, modify statistical measurements, and degrade analytical model correctness. As a result, identifying and dealing with outliers is crucial for generating trustworthy insights and making data-driven decisions.