A Multidimensional Integrated Dataset of Behavioral and Performance for Academic Student (Education)

This dataset comprises 19,222 observations synthesized through a robust Medallion Architecture in a SQL Server environment. By integrating data from three primary educational sources Student Productivity, Exam Prediction, and Student Performance it provides a multidimensional view of academic success factors. Key Analytical Features: Behavioral: Study hours, sleep patterns, and digital consumption (gaming/social media). Physiological: Self-reported stress levels and focus metrics. Academic: Historical performance (Previous_Grades) and attendance rates. Demographics: Age-binned cohorts and gender distribution. The dataset serves as a high-integrity foundation for supervised machine learning, offering both binary (is_passing) and multi-class (final_grade) target variables to support nuanced classification tasks.