This paper develops a model that best predicts the diagnosis of a patient as to whether they have liver disease as well as figure out which features of the nine listed contribute the most to a positive liver disease diagnosis. The dataset used contains many different biological factors that play a role in influencing the liver. Creating a model that can accurately predict the diagnosis of a patient can be a helpful tool in the medical field as Machine Learning models with high predicting accuracy can detect intricate patterns and help to reduce the number of False Negative diagnoses that occur. In addition, knowing which particular feature contributes the most to liver disease can help doctors prioritize the normalization of certain feature levels to reduce the severity of the Liver Disease.