Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques

-Data Mining is a powerful tool for academic intervention. 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 our work, we collected the student’s data from engineering institute that have different information about their previous and current academics records like students S.No., Name, Branch, 10, 12 , B.Tech passing percentage and final grade & then apply different classification algorithm using Data Mining tools (WEKA) for analysis the students academics performance for Training & placement department or company executives. This paper deals with a comparative study of various classification data mining algorithms for the performance analysis of the student’s academic records and check which algorithm is optimal for classifying students’ based on their final grade. This analysis also classifies the performance of Students into Excellent, Good and Average categories. Keywords– Data Mining, Discover knowledge, Technical Education, Educational Data, Mining, Classification, WEKA, Classifiers.

[1]  Sunita B Aher,et al.  Data Mining in Educational System using WEKA , 2011 .

[2]  Tongshan Chang,et al.  Data Mining: a Magic Technology for College Recruitment , 2009 .

[3]  Barry G. Becker Visualizing decision table classifiers , 1998, Proceedings IEEE Symposium on Information Visualization (Cat. No.98TB100258).

[4]  V. Ramesh,et al.  Performance Analysis of Data Mining Techniques for Placement Chance Prediction , 2011 .

[5]  Karl Rihaczek,et al.  1. WHAT IS DATA MINING? , 2019, Data Mining for the Social Sciences.

[6]  Umesh Kumar Pandey,et al.  Data Mining : A prediction of performer or underperformer using classification , 2011, ArXiv.

[7]  Vijayalakshmi,et al.  Implication Of Classification Techniques In Predicting Student’s Recital , 2011 .

[8]  Sebastián Ventura,et al.  Data mining in course management systems: Moodle case study and tutorial , 2008, Comput. Educ..

[9]  Karin Becker,et al.  Distance education: a Web usage mining case study for the evaluation of learning sites , 2003, Proceedings 3rd IEEE International Conference on Advanced Technologies.

[10]  George Karypis,et al.  Gene classification using expression profiles: a feasibility study , 2001, Proceedings 2nd Annual IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2001).

[11]  R. S. Bichkar,et al.  Performance Prediction of Engineering Students using Decision Trees , 2011 .

[12]  Varsha Namdeo,et al.  Result Analysis Using Classification Techniques , 2010 .

[13]  P. K. Srimani,et al.  Data Mining Techniques for the performance Analysis of a Learning Model A Case Study , 2012 .

[14]  Alaa M. El-Halees Mining students data to analyze e-Learning behavior: A Case Study , 2009 .

[15]  Seema Purohit,et al.  Prediction of Final Result and Placement of Students using Classification Algorithm , 2012 .

[16]  Usama M. Fayyad,et al.  Knowledge Discovery in Databases: An Overview , 1997, ILP.

[17]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[18]  Saurabh Pal,et al.  Mining Educational Data to Analyze Students' Performance , 2012, ArXiv.

[19]  Joseph P. Bigus,et al.  Data mining with neural networks , 1996 .

[20]  Jack Mostow,et al.  Some useful tactics to modify, map and mine data from intelligent tutors , 2006, Natural Language Engineering.

[21]  Jyoti Vashishtha,et al.  A Generalized Data mining Framework for Placement Chance Prediction Problems , 2011 .

[22]  Qasem A. Al-Radaideh,et al.  Mining Student Data Using Decision Trees , 2006 .

[23]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[24]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .