Open AccessComparison of Some Classification Algorithms for the Analysis of Students Academic Performance in Educational Data Mining Using Orange
Author(s) -
Vanthana
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1394
Subject(s) - random forest , decision tree , computer science , naive bayes classifier , support vector machine , data mining , educational data mining , machine learning , orange (colour) , statistical classification , decision tree learning , artificial intelligence , process (computing) , data science , biology , horticulture , operating system
In the modern education system, many higher education institutions prefer data mining tools and techniques to analyze the academic improvement of their students. To support that many data mining techniques and tools are available. This paper uses the classification concept to analyze the student’s academic performance. This paper presents the comparison result of five classification algorithms – Decision Tree, Naïve Bayesian, K-Nearest Neighbour, Support Vector Machine and Random Forest which is applied to the data collected from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. The data mining tool used was ORANGE. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in the dataset. The results showed that Random Forest out performs the other classifiers based on accuracy.