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