Fill in the Gap: Two-Stage Prediction Modeling Framework for Understanding Human Biosensor Data

Human behaviors can be understood from various forms of information such as sleep, emotions or health conditions. With the aid of advanced technology, sensors can capture the conditions of humans and provide valuable insights. In this work, we used imputation strategies to overcome the challenge of incomplete data records and feature misalignment. A two-stage modeling of a Random Forest classifier was done to make final predictions on the test set. This relatively simple yet heavily data-centric approach leverages the strengths of multi-output classification and sequential learning by enhancing data integrity and feature consistency.