Machine Learning is defined as nothing but as like humans learn from their experience likewise machine learns from experience. Here experience is nothing but the training and testing the machine with the. There are many techniques are available to train and test the system like Data Mining algorithms, Machine Learning Algorithms and Deep Learning Algorithms. It is not that all the algorithms will provide better results. Also there are many kinds of datasets is available. In this paper, the main focus is on Medical Dataset which requires more attention nowadays. And the algorithm we focus is on Machine Learning which contains different classification algorithms. Applying all the classification algorithms to the dataset and finding the best algorithm for the medical dataset with the highest accuracy. We have trained the dataset using seven classification algorithms called Naive Bayes, Random Forest, Support Vector Machine, Decision Tree, KNN, KSVM. After the implementation of each algorithm, we came up with a conclusion that Random Forest is the best algorithm for medical dataset which gives 100%accuracy among these seven Classification algorithms.