Classification of Student Performance Dataset using Machine Learning Algorithms
K. Maheswari1, P. Deepalakshmi2, K. Ponmozhi3
1Dr. K. Maheswari, Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankovil Virudhunagar (Tamil Nadu), India.
2Dr. P. Deepalakshmi, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankovil Virudhunagar (Tamil Nadu), India.
3Dr. K. Ponmozhi, Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankovil Virudhunagar (Tamil Nadu), India.
Manuscript received on 07 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 30 December 2019 | PP: 752-757 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11141292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1114.1292S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The scope of this research work is to identify the efficient machine learning algorithm for predicting the behavior of a student from the student performance dataset. We applied Support Vector Machines, K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms to predict the grade of a student and compared their prediction results in terms of various performance metrics. The students who visited many resources for reference, made academic related discussions and interactions in the class room, absent for minimum days, cared by parents care have shown great improvement in the final grade. Among the machine learning techniques we have used, SVM has shown more accuracy in terms of four important attribute. The accuracy rate of SVM after tuning is 0.80. The KNN and decision tree achieves the accuracy of 0.64, 0.65 respectively whereas the Naïve Bayes achieves 0.77.
Keywords: Classification, Decision Tree , KNN, Machine Learning, Naïve Bayes, Student Performance and SVM.
Scope of the Article: Classification