Supervised learning is one of the most common machine learning tasks. Many machine learning algorithms follow the structure of supervised learning such as K Nearest Neighbour (KNN), Decision Tree, Support Vector Machine, and others. Every algorithm is designed based on special style which can perform well in some cases and not in other cases. The performance depends on the data type, class distribution, or data quality. In this study, three machine learning algorithms were compared which are: K Nearest Neighbour (KNN), Decision Tree, and Support Vector Machine. The experiments were handled using three datasets named: Algerian Forest Fires, Online Shoppers Purchasing Intention, and Heart Failure Clinical Record. In this study, the model is trained based on instances supported by a class label. The results were obtained after utilizing the three algorithms on these datasets. These algorithms were ranked differently of each experiment based on the data type.