Anomalies Detection

Anomalies detection is the process of identifying data points that deviate from the expected pattern or behavior of a given dataset. In data science and artificial intelligence, anomalies detection is used to identify outliers, errors, or unusual events that may indicate a problem or an opportunity for further investigation. Anomalies detection can be performed using various techniques such as statistical methods, machine learning algorithms, or rule-based systems. The goal of anomalies detection is to improve the accuracy and reliability of data analysis, decision-making, and predictive modeling by identifying and removing or correcting anomalous data points.
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