Stress Analyzer: A Machine Learning Approach to Predict Student Stress Levels for Early Intervention

Stress Analyzer is a machine learning-based research project developed to predict stress levels among university students for early intervention. The study analyzes multiple factors including academic workload, sleep quality, social support, and financial conditions to classify stress into low, medium, and high levels. Machine learning algorithms including Random Forest, Decision Tree, and Linear Regression were evaluated using a dataset of over 1,000 student responses. The objective is to provide universities with an intelligent decision-support tool to identify students at risk and improve student well-being through timely interventions.