Mental Health Companion

Abstract

This research endeavors to address the critical challenge of timely identification and intervention in mental health disorders, particularly depression. Utilizing self- reported symptoms obtained through survey responses, alongside essential demographic and personal data, we aim to develop a predictive model for the early detection of mental health issues. The dataset, comprising diverse survey responses, provides a comprehensive understanding of participants' mental well-being. Employing advanced machine learning techniques, we seek to uncover patterns that can contribute to an effective prediction model. The ultimate aspiration is to empower healthcare practitioners with a sophisticated tool that enhances the overall efficacy of mental health interventions. By navigating the complexities of mental health identification, our research underscores the importance of proactive measures, striving for a paradigm shift towards a more comprehensive and timely approach to mental healthcare. Through systematic testing and model selection, we aim to pave the way for a transformative tool that can positively impact public health by enabling early detection and intervention. Key Words: Mental Health Prediction, Depression, Random Forest Classifier, Decision Tree Classifier, Gaussian Naive Bayes, K-Nearest Neighbors, Machine Learning, Classification, Anxiety, Stress, Mental Health.