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Analyzing the Health of Engineering Student's using Feature Selection Algorithms - A Comparative Study

Author(s):

G. Ramya , JAMAL MOHAMMED COLLEGE, TRICHY; S. kavitha, JAMAL MOHAMMED COLLEGE, TRICHY

Keywords:

collection, extraction, warehousing, analysis, statistics, interestingness metrics, complexity considerations

Abstract

In this paper, data mining study about the Health of Undergraduate Engineering Students. This dissertation use the feature selection algorithm based on clustering methods to analyze the importance of health and well-being in students is exemplified by the large number of studies on this topic. Past research has focused on using surveys to identify factors that affect the health, but applying machine learning tools to such data has not received much attention. Moreover this dissertation presents the comparative study between the Correlation based feature selection algorithm and the Minimum Redundancy and Maximum Relevance feature selection (mRmR), these two algorithms can be applied to the engineering students based data, for analysis the health.

Other Details

Paper ID: IJSRDV2I6264
Published in: Volume : 2, Issue : 6
Publication Date: 01/09/2014
Page(s): 713-716

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