AI and ML Functionality in Arbutus Analyzer

This technical report introduces the AI and ML functionalities available in the Arbutus Analyzer, detailing various applications designed to enhance data analysis capabilities. The report outlines several AI-ML applications, including Outliers, Clusters, Sentiment Analysis, Smart Query, Data Categorization, and Arbutus Assistant. Each application utilizes specific Python procedures to perform data analysis, identify patterns, and detect anomalies. The report emphasizes the importance of addressing outliers in datasets, explaining their definitions, causes, and the necessity of their removal for accurate analysis. It presents different statistical methods for identifying outliers, such as the Z-Score, Modified Z-Score, and Interquartile Range methods. Additionally, the report describes how Arbutus technology facilitates the identification of outliers through its ML functionality, ensuring that data processing remains secure on customer premises. The document serves as a comprehensive guide for users to understand and utilize the AI-ML capabilities within the Arbutus Analyzer effectively.