• Nidhi R Shetty
• Shreya Raj
• Vartika Sharma
• Susmita Debnath
• K P Mayuri

This paper highlights the significance of digital transformation particularly in the sphere of mental healthcare. Mentalhealth influences how one feels and behaves. Bad mental health can lead to depression. Its early detection enables doctorstodiagnose them more effectively. This paper describes the development of an application to track an individual’s mental health.Several accuracy criteria were used to assess the efficacy of four machine learning techniques in this project's predictionof mentalhealth stability. KNN Classifier, Decision Tree Classifier, Logistic Regression, and SVM are the four MachineLearningalgorithms. We compared these methods, put them into practice, and found the most accurate method.

Machine Learning, Mental Health, Accuracy, Machine Learning Algorithms, SupervisedLearning,Depression, Psychological Stability, Prediction.

"Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.a45-a48, May-2023, Available :http://www.jetir.org/papers/JETIR2305007.pdf

"Mental Health Tracker For User’s Well-Being Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppa45-a48, May-2023, Available at : http://www.jetir.org/papers/JETIR2305007.pdf