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Hepatitis C virus is a virus-borne infection that attacks the liver and causes inflammation. Disease infection is caused due to exposure to infectious blood, such as from sharing needles or using unsterile tattoo equipment. Identification of this disease is a challenging task as it has no predefined symptoms. The machine learning technology could help with the analysis of clinical parameters for the classification of Hepatitis C virus. With this review, this project aims to predict the presence of Hepatitis C virus by using Hepatitis C virus dataset retrieved from the KAGGLE machine learning repository. The Hepatitis C virus sample has been preprocessed with encoding and incomplete data. It has 12 attributes and 615 individual person records. To examine the outcome measures, the original database is fed to all classifier models. To look at how the target feature variable is distributed, exploratory data analysis is done. The …