A Machine Learning Practice on NAS Dataset: Influence of Socioeconomic Factors on Student Performance
Kotrike Swetha1, M. Imtiaz Ur Rahaman2
1Kotrike Swetha, (MTech), Department of CSE, G. Pulla Reddy Engineering College, Autonomous, Kurnool.
2M. Imtiaz Ur Rahaman, Associate Professor, Department of CSE, G. Pulla Reddy Engineering College, Autonomous, Kurnool.
Manuscript received on 01 March 2019 | Revised Manuscript received on 06 March 2019 | Manuscript published on 30 July 2019 | PP: 3272-3275 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1652078219/19©BEIESP | DOI: 10.35940/ijrte.B1652.078219
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© 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: India’s population is enormous and diverse due to which its education system is very complex. Furthermore, due to several reasons that they have grown up in different environmental situations. Over the years, several changes have been suggested and implemented by various stakeholders to improve the quality of education in schools. This paper presents a novel method to predict the performance of a new student by the analysis of historical student data records, and furthermore, we explore the NAS dataset using cutting edge Machine Learning Algorithms to predict the grades of a new student and take proactive measures to help them succeed. Similarly, NAS Dataset can also be worthwhile to the employee dataset and can predict the performance of the employee. Some of the Supervised Machine Learning Algorithms for Classification which have been successfully applied to the NAS dataset. Support Vector Machines and K-Nearest Neighbours algorithms did not crop results in coherent time for the given dataset; Gradient Boosting Classifier outperformed than all other algorithms reliably.
Keywords: Indian Education System, National Achievement Survey, Machine Learning Algorithms, Supervised Learning, Gradient Boosting Classifier
Scope of the Article: Machine Learning