Machine learning is increasingly being explored for applications in healthcare and wellbeing. However, building an ML model for a sensitive domain such as mental health requires more than simply achieving a high accuracy score. I developed a Mental Health Prediction & Assessment System that combines machine learning, PHQ-9 screening, Explainable AI and a Flask-based web application. The project is available on GitHub: https://github.com/starJeet000/Mental-Health-Prediction-Using-Machine-Learning What Is the Project? The application is designed as an educational and preliminary screening system that evaluates mental-health-related information and produces a risk prediction. It combines an ML-based prediction system with a standardized PHQ-9 questionnaire. The purpose is not to replace mental-health professionals but to demonstrate how machine learning can be incorporated into a complete software application. Machine Learning Model Several classification algorithms were tr...
What if your Telegram bot could do more than respond to commands? I built a Telegram-based AI motivation system that generates personalized motivational messages, schedules them automatically, tracks user streaks, collects feedback, and continues operating even when the AI service becomes unavailable. The project is called Telegram Daily Motivation Bot . What Does the Bot Do? The basic idea is simple: Generate useful motivation → deliver it automatically → track engagement → learn from feedback. Users can interact with the bot directly through Telegram. Some of the available commands include: /motivate — generate an instant motivational message /today — retrieve the latest motivation /subscribe — subscribe to automated messages /unsubscribe — stop automated messages /history — view recent generated messages /stats — check engagement information /leaderboard — view the global streak leaderboard /schedule — configure delivery periods /set_tone — customize the motivational style ...