Student Academic Performance and Lifestyle Dataset (Gold Layer)

Description

This dataset contains 19,222 anonymized student records, designed for predicting academic performance using a multiclass classification approach (Grades: A, B, C, D, F). It was constructed using a Medallion Architecture data engineering pipeline, representing the final 'Gold Layer'. The dataset integrates 14 key features encompassing students' daily lifestyle habits (e.g., study hours, sleep duration, gaming hours), psychological indicators (e.g., stress levels, focus scores), and demographic/academic backgrounds. It is highly optimized and calibrated for advanced deep learning applications in Educational Data Mining (EDM).

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