MindGuard: Digital Aid for Smartphone Addiction

In a time when smartphone use and digital connectivity are ubiquitous, worries about excessive screen time and its effects on mental health are becoming more and more pressing. Our project's main goal is to develop MindGuard, a sophisticated machine learning and natural language processing tool that employs Random Forest and AdaBoost to forecast and analyze usage trends in order to solve this problem. The system monitors information about smartphone usage, such as communication patterns, screen duration, and application interaction. Textual information is analyzed using NLP techniques to identify behavioral clues that may indicate addiction. Using this data, machine learning algorithms such as Random Forest and AdaBoost are used to create predictive models that determine usage patterns linked to addiction and evaluate risk. Through the integration of various technologies, the system offers users individualized feedback and real-time information, empowering them to successfully understand and change their smartphone habits. With its all-encompassing approach to tracking, anticipating, and treating smartphone addiction, this cutting-edge method encourages better digital habits and overall wellbeing.