Student Health and Stress Prediction System using Machine Learning

Abstract: This study introduces a Student Health and Stress Prediction System that uses machine learning to better understand students’ well-being. It looks at everyday habits like sleep, study time, screen usage, and physical activity to identify patterns related to stress. The data is collected through surveys and carefully processed to ensure it is accurate and useful. By applying machine learning techniques, the system can categorize students into different stress levels such as low, moderate, or high. Statistical methods are used to check how reliable the predictions are. The system also helps in detecting early signs of stress and suggests simple ways to improve daily routines. Overall, this approach supports students in maintaining better mental health, leading to improved well-being and academic performance.

Keywords: Student stress detection, machine learning techniques, monitoring of mental health, analysis of daily lifestyle habits, data-driven insights.