An Ingenious Methodology for the Collation of Existing Algorithms for the Prognosis of Student Performance
Arya B V1, Amritha P B2, C V Prasanna Kumar3
1Arya B V*, PG Student Assistant Professor Department of Computer Science and IT, Amrita School of Arts And Sciences, Kochi Amrita Vishwa Vidyapeetham, India.
2Amritha P B , PG Student Assistant Professor Department of Computer Science and IT, Amrita School of Arts And Sciences, Kochi Amrita Vishwa Vidyapeetham, India.
3C V Prasanna Kumar, PG Student Assistant Professor Department of Computer Science and IT, Amrita School of Arts And Sciences, Kochi Amrita Vishwa Vidyapeetham, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 24, 2020. | Manuscript published on March 10, 2020. | PP: 1749-1752 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2874039520/2020©BEIESP | DOI: 10.35940/ijitee.E2874.039520
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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: In this proposed research work we use a profound Data mining technique which is an automated procedure of discovering interesting patterns by means of comprehensible predictive models from large data sets by grouping them. Predicting a student’s academic performance is very crucial especially for universities. Educational Data Mining (EDM) is an approach for extricating useful data that could possibly affect a firm. Nowadays student’s performance is swayed by a lot of aspects. These aspects might involve the academic performance of a student. This subject evaluates numerous factors probably suspected to alter a student’s empirical performance in scholastic, and discover a subjective design which classifies and forecast the student’s learning outcomes. The intention of this research is to conduct a case study on factors swayed by the student’s academic achievements and to dictate greater impact factors. In this paper we focus on the academic achievement evaluation on the basis of correct instances and incorrect instances by means of Naive Bayes and Random Forest algorithms. This paper intends to make a metaphorical assessment of Naive Bayes and random Forest classifier on student data and dictate the best algorithm.
Keywords: Naïve Bayes, Random Forest, Educational Data Mining.
Scope of the Article: Forest Genomics and Informatics