Abstract: AI application development often begins with an initial dataset and a clear vision for the desired analytic outcomes. However, despite these foundational elements, practitioners typically encounter a prolonged and labor-intensive phase known as data conditioning. This phase encompasses essential data engineering processes, including data preprocessing, anomaly resolution, and the selection of suitable algorithms tailored to specific analytical goals.