An Experimental Analysis of Various Algorithms for Classification in Educational Data Mining with the help of LMS
Devika Radhakrishnan1, Shubhangi Neware2
1Devika Radhakrishnan M. Tech Department of CSE Shri Ramdeobaba college of Engineering & Management, Nagpur, India.
2Dr Shubhangi Neware, Assistant Professor Department of Computer Science, Shri Ramdeobaba college of Engineering & Management, Nagpur, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 271-276 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10750688S319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data in educational institutions are developing continuously and rapidly along these lines there is a need of advancement, that this large and excessive amount of data is to be converted into helpful data and need of implementing data mining technique. Educational data mining is concerned with the application of various statistical analysis, data mining technique, machine learning which will be helpful for school, colleges and universities Educational data mining is the zone of science where various kinds of techniques are being created for analysis, looking and investigating data and this will be valuable for better comprehension for the further studies and the settings they learned. As the data is predefined, the classification of object in the view is data mining and information management procedure is utilized as a part of comparable data questions altogether. Decision Tree is a very valuable and well-known classification method that helps in decision making based on the possible consequences, that can be event outcomes, resource costs or utility. It contains conditional control statements. One explanation behind its noticeability comes from the accessibility of existing calculations that can be utilized to assemble decision trees. In this paper we will survey the different ordinarily utilized decision tree calculations which are utilized for classification.One application behind its noticeability comes from the accessibility of existing calculations that can be utilized to assemble decision trees. In this paper we will survey the different ordinarily utilized decision tree calculations which are utilized for classification. We will likewise be thinking about how these decision tree calculations are done and are made appropriate and valuable for educational data mining and which one is ideal.
Keywords: Educational data mining (EDM), Classification, Dropout Prediction, decision tree, learning management system.
Scope of the Article: Data Mining