Developed a statistical and machine learning framework to analyze factors associated with zoonotic virus pathogenicity and potential human infection risk. The project focuses on applying statistical modeling, exploratory data analysis, feature selection, and machine learning techniques to identify meaningful patterns and build predictive models.