During the recentdecades, the risk of Liver disease in people is increasing at a rapid rate and is sought to beone of the fatal diseases in the world. It’s quite a difficult task for researchers to predict the disease fromhumongous medical databases. To combat this issue, they have come up with machine learning techniques likeclassification and clustering. The main aim of this Research is to predict the chances of a patient having a liverdiseaseusingtheclassificationalgorithms. And it identify the stage of Liver disease like 1- Cirrhosis Liver, 2-Liver fibrosis, 3-Fatty Liver, 4-Healthy Liver. So NB, SVM, LOR,RF,DT,KNN, RBTC thesealgorithmsarecomparedwith proposed Hybrid Classifier(RF,SVC,XGBoost)basedontheirclassificationaccuracy and execution time. With these performance factors taken into consideration, the Hybrid Classifier whichserves as a better classifier is chosen with 99% accuracy.

LogisticRegression,NeuralNetwork,Dataset,Accuracy,SVM, HYBRID model