Artificial Intelligence and Machine Learning Model Implementation for UC Irvine Dataset 322

The dataset, sourced from the UCI repository, underscores the value of open-source resources in advancing sensor research. In this study, raw sensor data were compressed using polynomial fitting (polyfit), achieving nearly a 100-fold reduction, and subsequently analyzed with the Isolation Forest algorithm to detect anomalies. Python libraries, including scikit-learn, were deployed in an isolated environment to ensure reproducibility and scalability. The workflow was further integrated into Microsoft Copilot, enabling natural language interaction with the AI agent for streamlined big data modeling.