This project explores liver disease prediction using a dataset from Kaggle, applying machine learning techniques to classify patients based on key health indicators. The analysis involves comprehensive data exploration, preprocessing, and feature engineering to prepare the dataset for modeling. Two predictive models—logistic regression and decision trees—are implemented in R to assess their effectiveness in identifying individuals at risk of liver disease. Performance evaluation metrics, including accuracy, precision, and recall, are used to compare model effectiveness.