Human beings, the liver is the most primary part of the body and performs many functions, including the production of bile, excretion of bile and bilirubin, metabolism of proteins and carbohydrates, activation of enzymes, storage of glycogen, vitamins, and minerals, plasma protein synthesis, and the production of clotting factors. The liver is easily harmed by the consumption of alcohol, pain reliever tablets, and a variety of wired practices. Currently, liver-related diseases are identified by analysing liver function blood test reports and scan reports. It takes more time and is more expensive. While employing different data mining algorithms to ease this process, it is possible to reduce the time for diagnosing liver disease. the machine learning model made before us for prediction for liver disease were a very good models but their accuracy is very low and it was not available for everyone or common people, Also it is the fact that when more data is used, The prediction will be more accurate and to make sure of that we have created multiple machine learning model to get the best accuracy, model like Random Forest, Decision Tree, SDG Classifier, Grid Search, Logistic Regression, XG Boost all this models gave accuracy between 60% -70%, then we also used SVM which gave the highest accuracy of 80% and deployed this model so that every common people can you it..

Machine Learning, Liver Disease, SDG Classifier, XG Boost, Accuracy, Random Forest, Decision Tree, Support Vector Machine.