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Student Intervention System using Machine Learning Techniques
Shubhangi Urkude1, Kshitij Gupta2
1Shubhangi Urkud, Department of Computer Science and Engineering Science and Technology, The ICFAI Foundation for Higher Education, Hyderabad (Telangana), India.
2Kshitij Gupta, Department of Computer Science and Engineering Science and Technology, The ICFAI Foundation for Higher Education, Hyderabad (Telangana), India.
Manuscript received on 01 November 2019 | Revised Manuscript received on 13 November 2019 | Manuscript Published on 22 November 2019 | PP: 2061-2065 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F13920986S319/19©BEIESP | DOI: 10.35940/ijeat.F1392.0986S319
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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: Now a days, the educational institutes are adopting technologies for betterment of student’s quality, in respect to teaching methodologies etc. For which the huge information available with educational institutes can be used to predict student’s future in academics. The main objective of this paper is to predict the student performance in the examination and also to predict the student will graduate or not. Hence forth we are using statistical analytical method which is F1 score. F1 score or F measure is used to test the prediction accuracy by considering precision and recall to compute the score. To fulfill this requirement in machine learning, classification technique is used. The dataset used in this analysis contains 395 student records, having attributes, such as age, health, internet, school, father job, mother job etc. Using support vector machines (SVM), Decision Tree and Naïve Bayes (NB) classification algorithms F1 score is calculated for each algorithm. Based on the analysis done the F1 score of support vector machine is giving the better prediction compared to rest of the two algorithms.
Keywords: Support Vector Machines (SVM), Decision Tree, Naïve Bayes (NB), Classification Algorithm, Prediction.
Scope of the Article: Machine Learning