Predicting Digital Addiction Patterns with Machine Learning for Personalised Mental Health Support"

The project titled "Predicting Digital Addiction Patterns with Machine Learning for Personalised Mental Health Support" aims to predict the mental health status of individuals based on various lifestyle and technology usage factors. The dataset includes features such as age, technology usage hours, social media usage hours, gaming hours, screen time hours, sleep hours, physical activity hours, gender, stress level, support systems access, work environment impact, and online support usage. The system uses machine learning algorithms like SVM, Random Forest, and a hybrid model combining CatBoost and GNN for predictions. The backend of the system is built using Django, and the frontend uses HTML, CSS, and JavaScript. The project is designed to offer personalized mental health support by analyzing digital addiction patterns and providing predictive insights into mental well-being.

Keywords: Digital addiction, mental health, machine learning, SVM, Random Forest, CatBoost, GNN, prediction, lifestyle, technology usage.