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Student Mental Health and Burnout Dataset
Student Mental Health and Burnout is a large-scale synthetic dataset designed to analyze and predict student burnout levels through academic, psychological, and lifestyle factors. The dataset contains 150,000 student records, combining numerical and categorical features, making it suitable for machine learning, classification, and data analysis tasks.
Dataset composition:
- Demographic information: age, gender, course, grade
- Academic metrics: CGPA, attendance, study time
- Psychological rating: Anxiety, depression, stress level
- Lifestyle factors: sleep duration, physical activity, screen time
- Social and economic stress indicators
- Target variable: burnout_level
This dataset is contributed by community users and is intended for educational and informational purposes only. If any content involves copyright infringement, please contact us at [email protected] for prompt review and removal.
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