Estimation of Student Stress Prediction Using Machine Learning for MCA Students Under Pune University

Abstract: This study develops and compares machine-learning models to predict stress levels among MCA students under Pune University (SPPU) using a questionnaire-based dataset collected via Google Forms. The survey included 1000 responses covering demographics, academic, lifestyle, social, and personal factors. After preprocessing (cleaning, one-hot encoding, scaling), dimensionality reduction (PCA), and feature selection, four models were trained and evaluated: XG Boost, Random Forest, Principal Component Analysis + Support Vector Machine, and Logistic Regression. Models were assessed using accuracy, precision, recall, F1-score, confusion matrices, and ROC-AUC. Key predictors included sleep quality, family support, financial concerns, academic workload, and peer pressure. Among these XG Boost showed the best performance based on weighted F1-score and balanced accuracy. The findings provide insights for early stress interventions and student wellbeing programs.

Keywords: Student stress, mental health prediction, XG Boost, Random Forest, PCA, SVM, Logistic Regression, one-hot encoding, survey data, MCA students.