Leveraging Django and Machine Learning to Craft a Health Forecasting System, Tailoring Personalized Wellness Strategies

Abstract

Abstract: The goal of this thesis is to develop a health forecasting system that combines Django with cutting-edge machine learning in order to customize wellness plans according to personal health information. The study addresses model selection, data preprocessing, and user interface creation as it investigates the potential synergies between machine learning and web development. Predictive health analytics potential and gaps are identified through a review of the literature. The goal of the project is to create a real-time, scalable health forecasting system while protecting user privacy. Neural networks and decision trees are two examples of machine learning methods that are utilized. The efficacy of the system in delivering precise health projections and augmenting user involvement in proactive wellness management is evaluated through empirical assessment and case studies. In order to further personalized healthcare solutions, practitioners, researchers, and developers can benefit greatly from the research's insightful contributions.