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Analysis and Comparison of Classification Algorithms for Student Placement Prediction
Open Access   Article

Analysis and Comparison of Classification Algorithms for Student Placement Prediction
M. Shukla1 , A. K. Malviya2

Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-6 , Page no. 69-81, Jun-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6i6.6981

Online published on Jun 30, 2018

Copyright © M. Shukla, A. K. Malviya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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Citation

IEEE Style Citation: M. Shukla, A. K. Malviya, “Analysis and Comparison of Classification Algorithms for Student Placement Prediction”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.69-81, 2018.

MLA Style Citation: M. Shukla, A. K. Malviya "Analysis and Comparison of Classification Algorithms for Student Placement Prediction." International Journal of Computer Sciences and Engineering 6.6 (2018): 69-81.

APA Style Citation: M. Shukla, A. K. Malviya, (2018). Analysis and Comparison of Classification Algorithms for Student Placement Prediction. International Journal of Computer Sciences and Engineering, 6(6), 69-81.
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Abstract :
Educational data mining has gained importance for discovering the useful information from the student databases. It is observed that there is a lack of performance of the students during campus selection in technical institutions. Hence the problem highlighted in this research work is: “What factors are responsible for placement of some students but why not others during campus selection of technical institutions?” The objective of this research work is related to the prediction and discovery of the factors for student placement using the data mining techniques and tool. The methodology used in this research work involves four main stages to achieve the required objectives. They are Data Collection, Pre-processing, Classification and Interpretation of Result. The Classification algorithms used in this research paper include decision tree, Naive Bayes, Neural Network (Multilayer perceptron) and Sequential Minimal Optimisation. It has been found that Naive Bayes algorithm works best in student placement prediction with maximum accuracy. The identification of attributes is done using output decision tree model. After such findings, a classification system model is proposed which depicts the stages of pre-processing, attribute selection, classification, factor identification, factor improvement and placement prediction. It may also be applied at any institute where placement prediction is required before-hand to increase the chances of campus selection irrespective of courses. The classification model can be applied to the problems related to student placement at technical institutions.
Key-Words / Index Term :
Educational Data Mining, Placement chance prediction, Classification Algorithms, Attribute selection, Student Performance.
References :
[1] C. Anuradha, T. Velmurugan, “A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Students Performance”, International Journal of Science and Technology, Vol. 8, Issue 15, 2015.
[2] K.P. Chaudhari, R.A. Sharma, S.S. Jha, R.J. Bari, “Student Performance Prediction System using Data Mining Approach”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue 3, 2017.
[3] H. Hamsa, S. Indiradevi, J.J. Kizhakkethottam, “Student Academic Performance Prediction Model Using Decision tree and Fuzzy Genetic Algorithm”, Global Colloquium in Recent Advancement and Effectual Researches in Engineering, Science and Technology, 2016.
[4] R.R. Kabra, R.S. Bichkar, “Student`s Performance Prediction Using Genetic Algorithm”, International Journal of Computer Engineering and Applications, Vol. VI, Issue III, 2014.
[5] A. Katare, S. Dubey, “A Comparative Study of Classification Algorithms in EDM using 2 Level Classification for Predicting Student’s Performance”, International Journal of Computer Applications, Vol. 165, Issue 9, 2017.
[6] A. Mueen, B. Zafar, U. Manzoor, “Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques”, I. J. Modern Education and Computer Science, 2016.
[7] M. Mayilvaganan, D. Kalpanadevi, “Comparison of Classification Techniques for predicting the performance of Students Academic Environment”, International Conference on Communication and Network Technologies, 2014.
[8] A. Nichat, A.B. Raut, “Predicting and Analysis of student Performance Using Decision Tree Technique”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5,Issue 4, 2017.
[9] S. Rana, R. Garg, “Evaluation of Student`s Performance of an Institute Using Clustering Algorithms”, International Journal of Applied Engineering Research, Vol. 11, 2016.
[10] P. Revathy, P. Kalaiarasi, J. Kavitha, D.A. Madhumita, “Data Mining Approach for Suggesting Higher Education Courses Based on Student’s Performance”, International Journal of Science and Technoledge, Vol. 3, Issue 3, 2015.
[11] C. Romero, “Educational Data Mining: A Review of the State of the Art”, IEEE Transactions on Systems, Man, and Cybernetics- Part C: Applications and Reviews, Vol. 40, 2010.
[12] A.A. Saa, “Educational Data Mining and Students’ Performance Prediction”, International Journal of Advanced Computer Science and Applications, Vol. 7, 2016.
[13] R. Anupriya, P. Saranya, R. Deepika, “Mining Health Data in Multimodal Data Series for Disease Prediction”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issues 2, pp. 96-99, 2018.
[14] M. Fernandes, “Data Mining: A Comparative Study of its Various Techniques and its Process”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue 1, pp. 19-23, 2017.