The main objective of the higher educational organization is to provide high quality and necessary education to its students. The two goals of data mining in Indian education system is to analyze and enhance the chronicle way of recent educational data mining advances development; the second is to preserve, organize and discuss the content of the result which is produced by a data mining approach. The use of various data mining techniques such as random forest, decision tree, etc in Indian education processes will help to improve students' performance and provide a broad decision management skill in selection of courses as per their retention rate. This paper focuses on the model representation for analyzing the different data mining techniques in an Indian education system. Also the paper reviews a comparative study of ID3, K-Means, Naïve Bayes, Random Forest algorithm. In this paper, we have proposed the approach of Random Forest to predict the career decision for the 12 passing out students. The use of Random Forest has helped the students to take a correct appropriate decision as per their interest and skills and acts a career counselor toolbox.
[1]
Surjeet Kumar Yadav,et al.
Data Mining Applications: A comparative Study for Predicting Student's performance
,
2012,
ArXiv.
[2]
P. Veeramuthu,et al.
Analysis of Student Result Using Clustering Techniques
,
2014
.
[3]
Ismail M.A,et al.
Clustering Algorithms Applied in Educational Data Mining
,
2015
.
[4]
Nasri Harb,et al.
Factors Affecting Students' Performance
,
2006
.
[5]
Rajan Vohra,et al.
Applications of Data Mining in Higher Education
,
2012
.
[6]
K. Prasada Rao,et al.
Predicting Learning Behavior of Students using Classification Techniques
,
2016
.