Data engineering is a process involving extracting, transforming, and loading data to ensure that it is clean, reliable, and accessible in various analytical tasks. This research introduces an innovative approach to exploring nonverbal expressions in individuals with borderline personality disorder (BPD) using data engineering techniques. The study focuses on analyzing nonverbal cues exhibited by patients during a virtual tossing game to identify key factors influencing emotional responses in both inclusion and exclusion scenarios. By leveraging machine learning algorithms such as K-means, Decision Trees, and Random Forest, the research emphasizes the significance of head movement as a primary discriminator, followed by the task conditions and heart rate. Therefore, evidence of the effectiveness of using nonverbal expressions of patients with BPD by data engineering to offer featurization and new predictive values to support informed decisions of the clinicians is presented.