Analyzing Student’s Academic Performance Based on Data Mining Approach (International Journal of Innovative Research in Computer Science & Technology, 2017)
Career building is the most cherished part of  every engineering student. For an engineering graduate it is  necessary to have immense knowledge in their domain to get  placed in a reputed company. Data Mining is used to gain  knowledge, find the hidden information and also this system  applies data mining techniques to the academic dataset. The  Academic data includes the Internal (CCET 1, CCET2 and  CCET3) marks and the Assignment marks. The final  semester marks are predicted from the analyzed result of  each student. In order to increase the accuracy, this system  introduces reweight enhanced boosting algorithm.
Keywords: Data mining; Balanced Boosting; Educational data mining; Re-weight enhanced boosting; Ada-boosting