Due to the large volumes of data as well as the complex and dynamic properties data, data mining based techniques have been applied to datasets. With recent advances in computer technology large amounts of data could be collected and stored. Machine Learning techniques can help the integration of computer-based systems in any environment providing opportunities to facilitate and enhance the work of various industry professionals. It ultimately improves the efficiency and quality of data and information.

This paper presents the implementation of four supervised learning algorithms C4.5 Decision tree Classifier (J48), Instance Based Learning (IBK), Naive Bayes (NB) and Decision Stump in WEKA environment. The classification models were trained using various UCI datasets. The trained models were then used for classification & association which will help in decision making process. The Prediction Accuracy of the Classifiers was evaluated using 10-fold Cross Validation and the results have been compared to obtain the accuracy.

Keywords: Machine Learning, C4.5, NB, J48, IBK, Decision Stump, WEKA