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.
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DOI:
10.17148/IJARCCE.2025.141043
[1] Pranali Mahendra Ladkat, Ms. Deepali Gavhane, "Estimation of Student Stress Prediction Using Machine Learning for MCA Students Under Pune University," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141043
