Classification Model of Prediction for Placement of Students

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Ajay Kumar Pal 1,* Saurabh Pal 2

1. Sai Nath University, Ranchi, Jharkhand

2. Department of MCA, VBS Purvanchal University, Jaunpur, India

* Corresponding author.


Received: 11 Aug. 2013 / Revised: 4 Sep. 2013 / Accepted: 3 Oct. 2013 / Published: 8 Nov. 2013

Index Terms

Knowledge Discovery in Databases, Data Mining, Classification Model, Classification, WEKA


Data mining methodology can analyze relevant information results and produce different perspectives to understand more about the students’ activities. When designing an educational environment, applying data mining techniques discovers useful information that can be used in formative evaluation to assist educators establish a pedagogical basis for taking important decisions. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system.
In this study, we collected the student’s data that have different information about their previous and current academics records and then apply different classification algorithm using Data Mining tools (WEKA) for analysis the student’s academics performance for Training and placement.
This study presents a proposed model based on classification approach to find an enhanced evaluation method for predicting the placement for students. This model can determine the relations between academic achievement of students and their placement in campus selection.

Cite This Paper

Ajay Kumar Pal, Saurabh Pal, "Classification Model of Prediction for Placement of Students", International Journal of Modern Education and Computer Science (IJMECS), vol.5, no.11, pp.49-56, 2013. DOI:10.5815/ijmecs.2013.11.07


[1]Witten, I.H. & Frank E., Data Mining– Practical Machine Learning Tools and Techniques, Second edition, Morgan Kaufmann, San Francisco, 2000.
[2]Quinlan, J.R., C4.5: Programs for machine learning, Morgan Kaufmann, San Francisco, 1993.
[3]Wu, X. & Kumar, V., The Top Ten Algorithms in Data Mining, Chapman and Hall, Boca Raton. 2009.
[4]J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
[5]B.K. Bharadwaj and S. Pal., Data Mining: A prediction for performance improvement using classification”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 9, No. 4, pp. 136-140, 2011.
[6]Tongshan Chang, Ed.D, Data Mining: A Magic Technology for College Recruitment‛s, Paper of Overseas Chinese Association for Institutional Research (, 2008.
[7]U. K. Pandey, and S. Pal, A Data mining view on class room teaching language, (IJCSI) International Journal of Computer Science Issue, Vol. 8, Issue 2, pp. 277-282, ISSN:1694-0814, 2011.
[8]S. T. Hijazi, and R. S. M. M. Naqvi, “Factors affecting student’s performance: A Case of Private Colleges”, Bangladesh e-Journal of Sociology, Vol. 3, No. 1, 2006.
[9]Z. N. Khan, “Scholastic achievement of higher secondary students in science stream”, Journal of Social Sciences, Vol. 1, No. 2, pp. 84-87, 2005.
[10]Z. J. Kovacic, “Early prediction of student success: Mining student enrollment data”, Proceedings of Informing Science & IT Education Conference, 2010.
[11], “Examining online learning processes based on log files analysis: a case study”. Research, Reflection and Innovations in Integrating ICT in Education, 2007.
[12]S. K. Yadav, B.K. Bharadwaj and S. Pal, “Mining Educational Data to Predict Student’s Retention :A Comparative Study”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 10, No. 2, 2012
[13]Q. A. AI-Radaideh, E. W. AI-Shawakfa, and M. I. AI-Najjar, “Mining student data using decision trees”, International Arab Conference on Information Technology (ACIT'2006), Yarmouk University, Jordan, 2006.
[14]Sudheep Elayidom, Sumam Mary Idikkula & Joseph Alexander “A Generalized Data mining Framework for Placement Chance Prediction Problems” International Journal of Computer Applications (0975– 8887) Volume 31– No.3, October 2011.
[15]B.K. Bharadwaj and S. Pal. “Mining Educational Data to Analyze Students’ Performance”, International Journal of Advance Computer Science and Applications (IJACSA), Vol. 2, No. 6, pp. 63-69, 2011.
[16]Shaeela Ayesha, Tasleem Mustafa, Ahsan Raza Sattar, M. Inayat Khan, “Data mining model for higher education system”, Europen Journal of Scientific Research, Vol.43, No.1, pp.24-29, 2010.
[17]A. K. Pal, and S. Pal, “Analysis and Mining of Educational Data for Predicting the Performance of Students”, (IJECCE) International Journal of Electronics Communication and Computer Engineering, Vol. 4, Issue 5, pp. 1560-1565, ISSN: 2278-4209, 2013.
[18]M. Bray, The shadow education system: private tutoring and its implications for planners, (2nd ed.), UNESCO, PARIS, France, 2007.
[19]S. K. Yadav, B.K. Bharadwaj and S. Pal, “Data Mining Applications: A comparative study for Predicting Student’s Performance”, International Journal of Innovative Technology and Creative Engineering (IJITCE), Vol. 1, No. 12, pp. 13-19, 2011.
[20]Kappa at kappa.