Paper Title
Data-Driven Depression Prediction Using Bagging Algorithms: A Machine Learning Approach

Abstract
Depression is a common mental health illness, however its diagnosis remains subjective and often delayed. The paper provides a data-driven strategy employing machine learning, specifically the Bagging Classifier, to improve depression prediction accuracy. The model evaluates multiple datasets, including user surveys, clinical records, and behavioral indicators, enabling thorough risk assessment. Data preprocessing techniques such as normalization and noise removal increase input quality. The trained model is integrated into a web application for real-time depression risk evaluation, supporting mental health professionals in early identification. Experimental results demonstrate 90% accuracy, 93% precision, and 92% recall, showing the model's usefulness. This research underlines the potential of machine learning in mental health care while underlining ethical constraints in managing sensitive data. Future work includes incorporating additional behavioral markers and cloud-based deployment to improve scalability and accessibility. Keywords - Depression Prediction, Bagging Classifier, Data-Driven Diagnosis, Mental Health Assessment, Ensemble Learning