Prediction of Student Performance using Hybrid Classification
A.Dinesh Kumar1, R.Pandi Selvam2, V.Palanisamy3
1A.Dinesh Kumar, Research Scholar, Department of Computer Applications, Alagappa University, Karaikudi, India.
2R. Pandi Selvam, Assistant Professor and Head, PG Department of Computer Science, Ananda College, Devakottai, Tamilnadu, India.
3V. Palanisamy, Professor and Head, Department of Computer Aplications, Alagappa Univeristy, Karaikudi, Tamilnadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 6566-6570 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8241118419/2019©BEIESP | DOI: 10.35940/ijrte.D8241.118419

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Abstract: Data mining technologies allow collection, storage and processing huge amounts of data and carrying a large variety of data types and samples. Predicting academic performance of student is the most successive research in this era. Previous research work researchers are used different classification algorithm to predict the student performance. There is lot of research work to be taken in the field of educational data mining and big data in education to increase the accuracy of the classification algorithm and predict the academic performance of student. In this research work we used hybrid classification algorithm for predicting the performance of students. Two Popular classification algorithms ID3 and J48 were applied on the data set. To make hybrid classification voting technique is applied using weka machine learning tool. In this work we tested how the hybrid algorithm accurately predicts the student data set. To check the predicted result classification accuracy was computed. This hybrid classification algorithm gives accuracy with 62.67%.
Keywords: Big Data, Data Mining, Educational Data Mining (EDM), Hybrid Classification, Prediction.
Scope of the Article: Data Mining.