PBC (Primary Biliary Cholangitis) is a disease caused by the combination of genetic and environmental circumstances that slows down the immune system and attacks healthy cells of human’s body and destroys the bile duct of liver. It is one of the significant problems that has a heavier death rate. In this regard, the prediction of this disease in early stages can prevent the death conditions. In this context, this research focuses on classifying PBC outcome based on various health parameters. A comparative classification analysis is performed on an openly available PBC dataset. This dataset is pre-processed, imputed, and derived for the classification of Survival and non-survival of the patient being diagnosed. Classification algorithms like Decision Tree, SVM, Random Forest, Logistic Regression, Neural Networks are modeled for this task and SVM is found to have higher accuracy of 85.15 than other algorithms.