The higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The data consists of socio-economic, demographic as well as academic information of three hundred students with 22 attributes. Five ensemble classification methods Attribute Selected Classifier, Bagging, Classification Via Regression, Weighted Instances Handler Wrapper and Multi Class Classifier were used. The Class Attendance Percentage attribute makes the highest impact in the final semester results of the students in our dataset. The results showed that MultiClassClassifier outperforms the other classifiers based on accuracy and classifier errors.