It's been told from the beginning that the liver is one of the most important organs of our body function. Once upon a time, it couldn't see that a large number of people are suffering from liver diseases. But in recent years, the number of patients with liver diseases is increasing day by day. For this reason, affected people must go to a medical center for checking. In this paper, a model for the liver-affected people is implemented by which they do not need to go outside for checking the possibility of liver disease problems. To implement this model, datasets were collected based on some basic attributes which are related to liver diseases so that the possibility of liver problem can be detected. The data carries both liver-affected people and non-affected people. Those data were used to train our model so that the model can identify the affected people and non-affected people easily. Several machine learning algorithms were applied to generate results. The evaluation was done by following two approaches. Firstly, the complete result was generated using all of the attributes from both of the datasets where KNN provides the highest accuracy for both of the datasets which are 73% and 75.19% respectively. But after gone through an important attribute selection process, SVM gave the highest and increased accuracy which is about 82.68% and 81.15% respectively.