Open AccessPrediction of Student Performance using Hybrid Classification
Author(s) -
A.Dinesh Kumar*,
Rajajee Selvam,
V. Palanisamy
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrted8241.118419
Subject(s) - c4.5 algorithm , computer science , machine learning , statistical classification , id3 , data mining , educational data mining , field (mathematics) , artificial intelligence , set (abstract data type) , data set , decision tree , decision tree learning , support vector machine , naive bayes classifier , mathematics , pure mathematics , programming language
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%.