• Drashti Raval
• Viraj Parkhe
• Sunita Bangal

This research paper presents an in-depth exploration of data-driven mental health assessment, employing data science methodologies to develop predictive models for identifying depression, stress, and anxiety. Through meticulous data preprocessing, exploratory data analysis, and feature engineering, we transformed raw questionnaire data into a structured format conducive to analysis. Our study involved the evaluation of various machine learning models, including Random Forest, Decision Tree, Support Vector Machines, Logistic Regression, and k-Nearest Neighbors, through rigorous cross-validation. The results showcase the potential of these models for accurate mental health prediction. Additionally, ethical considerations surrounding privacy, consent, and potential misclassification are highlighted, emphasizing the importance of responsible model deployment in sensitive domains. This research contributes to the field by showcasing the power of data science in mental health assessment while acknowledging the ethical complexities associated with its implementation.

Data Science, Machine Learning, Predictive modeling, Depression, Stress, Anxiety, Logistic regression model, K-nearest neighbor classifier model, Support vector machine classifier, Decision tree model Random forest model, Mean accuracy, Precision, Recall, F-1 Score, Model evaluation, Cross validation, K-fold cross validation

"Detection of Mental Health Condition (Depression, Stress & Anxiety) using different machine learning models ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.a164-a174, September-2023, Available :http://www.jetir.org/papers/JETIR2309023.pdf

"Detection of Mental Health Condition (Depression, Stress & Anxiety) using different machine learning models ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppa164-a174, September-2023, Available at : http://www.jetir.org/papers/JETIR2309023.pdf