Women’s Wellness Guide Using Machine Learning

Women’s mental health is a critical yet often overlooked aspect of well-being. In this project, Women’s Wellness Guide, we have developed a comprehensive application designed to provide mental health support for women through innovative AI-driven features. Our system integrates machine learning models, utilizing logistic regression for sentiment analysis on journal entries, generating weekly mental health reports based on users’ emotional patterns. Additionally, a personalized recommendation system employs decision tree models to provide tailored suggestions based on age group (15– 19, 20–24, 25–30), mental state, daily routine, and severity of mental distress. Unlike existing systems that are largely static and provide only general information about mental health problems faced by women, our solution takes a dynamic approach. Using machine learning models, it offers personalized recommendations to address specific mental health challenges. The application incorporates real-time communication via Socket.IO, enabling seamless chat-based support. Furthermore, we offer professional consultation services and helpline support to assist users in distress. To enhance mental well-being, our platform provides curated content, including therapeutic music, informative articles, and guided exercises. This integrated approach leverages machine learning and real-time interaction technologies to offer personalized, accessible, and effective mental health support for women.