Students’ Performance Prediction Modelling using Classification Technique in R
Thingbaijam Lenin1, N. Chandrasekaran2 

1Thingbaijam Lenin, Research Scholar and Assistant Professor, Department of Computer Science, Martin Luther Christian University, Meghalaya, India
2N. Chandrasekaran, Visiting Professor, Martin Luther Christian University, Meghalaya, India

Manuscript received on 21 March 2019 | Revised Manuscript received on 27 March 2019 | Manuscript published on 30 July 2019 | PP: 5197-5201 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3259078219/19©BEIESP | DOI: 10.35940/ijrte.B3259.078219
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Abstract: Among several important tasks an academic institution performs, the most fundamental focus still remains very much on graduating best quality students. It then becomes of paramount importance to identify students whose performance is below par in order to help them to make them better learners. This study makes an earnest attempt to develop an automated system to tackle such a problem using a classification technique of Data Mining implemented with R programming language. Data pertaining to students’ demographic features, their previous academic records and personality traits were analyzed employing Random Forest, Naïve Bayes and K-Nearest Neighbors algorithms. The study shows that Personality, as defined by Myers-Briggs type indicator, influences the student’s performance. Random Forest is found to be the most promising algorithm for developing the students’ performance prediction system.
KEYWORDS: Classification Technique, Educational Data Mining, KNN, Naïve Bayes, Random Forest, R Programming.

Scope of the Article: Classification