Student Lifestyle Digital Twin and Impact Simulation System

The increasing prevalence of unhealthy lifestyle habits among students, including poor nutrition, inadequate sleep, physical inactivity, and high stress levels, can significantly impact overall health and well-being. Existing health applications primarily focus on activity tracking and lack the capability to predict and simulate the long-term impact of lifestyle choices. This research proposes a Student Lifestyle Digital Twin and Impact Simulation System, which creates a virtual representation of a user to analyze and simulate lifestyle effects over time. The system integrates Machine Learning techniques for predicting nutritional deficiencies and health risks, along with Digital Twin technology to simulate future wellness outcomes based on user inputs such as diet, exercise, sleep, hydration, and symptoms. Built using Flask, PostgreSQL, HTML/CSS, and data analysis tools such as Pandas and Matplotlib, the proposed system aims to provide personalized health insights, lifestyle recommendations, and predictive analysis to improve health awareness and proactive decision-making among students.