Predicting Student’s Academic Performance using Data Mining Techniques
Surbhi Agrawal1, Santosh K. Vishwakarma2

1Surbhi Agrawal, computer science, Gyan Ganga Institute of Technology and Sciences, Jabalpur, India.
2Santosh K. Vishwakarma, computer science, Manipal University, Jaipur, India.

Manuscript received on February 01, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 215-219 | Volume-9 Issue-3, February, 2020. | Retrieval Number: B4521129219/2020©BEIESP | DOI: 10.35940/ijeat.B4521.029320
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Abstract: To meet the change in world in terms of digitalization and progress, the need and importance of education is known to everyone. The increasing awareness towards and digitization has given rise to increase in size of education field’s database. Such database contains information about students. The information includes students behavior, their family background, the facility they have, the society environment which surrounds them, their academic records etc. The increasing technology in data sciences can help utilize this huge education field database in a productive way by applying data mining on it. When the techniques of Data mining are applied on the database relating education records, then this process is called as education data mining. This process helps us understand the area and the students on whom the attention and the amendments are required. This increases the level of education system and also affects the success rate and understanding of the students in academics in positive direction. In this paper four different classification algorithms are used to predict grades of the students, by referring student’s previous academic records. Out of the four algorithms, the one which gave the most accurate prediction is considered as the final prediction. The performance accuracy of different algorithm is compared through accuracy performance percentage.
Keywords: Accuracy performance percentage, Data mining, Algorithm, Classification, Education data mining, Data sciences.