This project aims to develop predictive models using advanced data science tools to address key challenges in health care, focusing on two areas: predicting 30-day patient readmissions and improving the Medicare Shared Savings Program (MSSP) Cost Model. By integrating social drivers of health (SDoH) data such as income, education and neighborhood into electronic medical records, the project seeks to enhance care delivery and population health. SDoH data provides insights into how a patient's environment and socioeconomic factors impact health outcomes, helping health care providers understand the root causes of health disparities. This initiative aims to improve healthcare efficiency, optimize resource allocation and reduce health care costs.