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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 8, August 2011
Performance Analysis Of Data Mining Techniques For Placement Chance Prediction
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Author(s)
V.Ramesh, P.Parkavi, P.Yasodha
KEYWORDS
Data Mining, Classification, Decision Tree Algorithm, placement Prediction
ABSTRACT
Predicting the performance of a student is a great concern to the higher education managements. The scope of this paper is to investigate the accuracy of data mining techniques in such an environment. The first step of the study is to gather student's data. We collected records of 300 Under Graduate students of computer science course, from a private Educational Institution. The second step is to clean the data and choose the relevant attributes. In the third step, NaiveBayesSimple, MultiLayerPerception, SMO, J48, REPTree algorithms were constructed and their performances were evaluated. The study revealed that the MultiLayerPerception is more accurate than the other algorithms. This work will help the institute to accurately predict the performance of the students.
References
[1] David Hand, Heikki Mannila, Padhraic Smyth ”Principles of Data Mining”

[2] Z. N. Khan, “Scholastic Achievement of Higher Secondary Students in Science Stream”, Journal of Social Sciences, Vol. 1, No. 2, 2005, pp. 84-87.

[3] Ye zhiwei, Hu zhengbing “Research on application data mining to teaching of basic computer courses in universities”

[4] Y. Ma, B. Liu, C.K. Wong, P.S. Yu, and S.M. Lee, “Targeting the Right Students Using Data Mining”, Proceedings of KDD, International Conference on Knowledge discovery and Data Mining, Boston, USA, 2000, pp. 457-464.

[5] S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Prediction of Student’s Performance in Distance Learning Using Machine Learning Techniques”, Applied Artificial Intelligence, Vol. 18, No. 5, 2004, pp. 411-426.

[6] P. Cortez, and A. Silva, “Using Data Mining To Predict Secondary School Student Performance”, In EUROSIS, A. Brito and J. Teixeira (Eds.), 2008, pp.5-12.

[7] Quinlan, J.R. (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo, CA.

[8] Burges, C.J.C. (1998) “A tutorial on support vector machines for pattern recognition.” Data Mining and Knowledge Discovery, Vol. 2(1), 121-167.

[9] Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo(1993)

[10] Weka – Data Mining Machine Learning Software, http://www.cs.waikato.ac.nz/ml/

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