Mental Health and Productivity Among Students

In recent years, the mental health of students has emerged as a critical concern affecting academic performance, motivation, and overall productivity. The pressures of academic workloads, social expectations, and lifestyle choices contribute significantly to students’ psychological well-being. This research paper explores the intricate relationship between mental health, lifestyle factors, and productivity among students using machine learning techniques. The primary objective of the study is to uncover patterns and correlations that may inform early intervention strategies and promote healthier academic environments. A comprehensive dataset was collected through surveys, incorporating various attributes such as sleep duration, screen time, physical activity, dietary habits, stress levels, anxiety indicators, study hours, and academic performance. Data preprocessing techniques were applied to clean and normalize the dataset. Several machine learning models, including Decision Trees, Random Forest, Logistic Regression, and Support Vector Machines, were implemented to identify the most significant factors impacting productivity and mental health. The study found strong correlations between mental health conditions such as anxiety and depression and lower levels of academic performance and daily productivity. Lifestyle choices like poor sleep, high screen time, and lack of physical activity were found to be major contributors to mental fatigue and reduced academic efficiency. Feature importance analysis highlighted that mental health scores, sleep patterns, and stress levels were among the most influential predictors of student productivity. The findings underscore the need for targeted mental health interventions and awareness programs within educational institutions. By leveraging machine learning for predictive analysis, this study offers a data-driven approach to understanding student well-being and optimizing academic outcomes. This research contributes to th