An Efficient Model for Predicting Student Dropout using Data Mining and Machine Learning Techniques
Mercy Paul Selvan1, Nagubadi Navadurga2, Nimmagadda Lakshmi Prasanna3
1Dr. Mercy Paul Selvan, School of Computing, Sathyabama Institute Of Science And Technology, Chennai (TamilNadu), India.
2Nagubadi Navadurga, School of Computing, Sathyabama Institute Of Science And Technology, Chennai (TamilNadu), India.
3Nimmagadda Lakshmi Prasanna, School of Computing, Sathyabama Institute Of Science And Technology, Chennai (TamilNadu), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 750-752 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11550789S219/19©BEIESP DOI: 10.35940/ijitee.I1155.0789S219
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Abstract: Education could be a important resource that has to lean to all or any kids. one in all the largest assets of the longer term generation cloud is alleged because the education that’s given to the youngsters. Most of the youngsters aren’t ready to continue their education because of many reasons. The prediction of student dropout plays a very important role in characteristic the scholars World Health Organization are on the sting of being a dropout from their education. whereas predicting this, we will simply try and solve their issues and create them continue their education. during this paper, we’ve planned a model for predicting the scholars can get born out or not mistreatment many machine learning techniques. we have a tendency to create use of decision trees that make a call mistreatment many factors. the choice of the prediction involves crucial wherever many knowledge attributes are used for prediction like correlations, similarity measures, frequent patterns, and associations rule mining. The planned work is evaluated mistreatment numerous parameters and is well-tried to figure expeditiously in predicting the dropout students compared with alternative.
Keywords: Education, Classification, Accuracy, Decision Trees, Prediction, Dropout, Machine Learning
Scope of the Article: Energy Efficient Building Technology