Published Paper Details:

The unique strategy to tracking and monitoring mental health that this paper presents makes use of data science and artificial intelligence/machine learning (AI/ML) approaches. The project's goal is to produce a complete mental health tracker system that can analyze various data sources and offer chances for early intervention and individualized insights. The system integrates information from several sources, including wearable technology, social media activity, calendar events, and self-reported mood assessments, by utilizing AI/ML algorithms. The tracker uses sophisticated data analysis and predictive modelling to find patterns, trends, and possible risk factors related to variations in mental health. In addition, the system makes use of feedback mechanisms and real-time monitoring to enable people to take proactive measures to maintain their mental health. The project offers a scalable and easily available solution for ongoing mental health monitoring and support, helping to close the gap between conventional mental health evaluation techniques and cutting-edge technologies.