Shankar-girish/HybridOutlierDetection: HybridOutlierDetection

HybridOutlierDetection is a hybrid analytical framework designed to identify anomalous patterns in complex datasets by combining multiple outlier detection techniques. The approach integrates statistical methods and machine learning models to improve detection accuracy and robustness. By leveraging the strengths of different algorithms, it effectively detects both global and local anomalies within large-scale data. The framework is particularly useful in domains such as fraud detection, network security, and financial analytics. Overall, HybridOutlierDetection provides a reliable and scalable solution for identifying unusual data behavior in real-world applications.