This project focuses on using data science techniques to build and analyze predictive models for disease prediction. In the final project, students select a type of predictive model, such as classification or regression, to predict the likelihood of a person developing a specific disease based on available medical data. The process includes data collection, preprocessing, model selection (e.g., Decision Trees, Random Forest, Logistic Regression), training the model, and evaluating its performance using metrics like accuracy, precision, and recall. The goal is to provide insights that can aid in early disease detection and preventive care, for conditions such as diabetes, cancer, or heart disease.