Enhancing the Quality Education using Predictive and Descriptive Data Mining Model
S. Jayakumar1, R. Parameswari2, A.Akila3

1Mr. S. Jayakumar, Department of Computer Science, Vels Institute of Science, Technology and Advanced Studies, Chennai, India.
2Dr. R. Parameswari, Department of Computer Science , Vels Institute of Science, Technology and Advanced Studies, Chennai, India.
3Dr.A.Akila, Department of Computer Science, Vels Institute of Science, Technology & Advanced Studies, Chennai, India. 

Manuscript received on 12 August 2019. | Revised Manuscript received on 18 August 2019. | Manuscript published on 30 September 2019. | PP: 6843-6847 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5795098319/2019©BEIESP | DOI: 10.35940/ijrte.C5795.098319
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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 mining is the trending field used to get relevant knowledge from the database given. This technique consists of subfield called educational data mining is the emerging area used to extract the hidden patterns from the huge data with the help of tools techniques developed by the researchers of the educational data mining. The purpose of extracting patterns from the educational database is to improve the quality of education can be provided to the students for their better feature. The patterns are extracted by using the existing data mining techniques to enhance student performance. Educational data mining techniques such as classification, regression, clustering are available in the field. Classification is defined as the technique used to categorize the data based on the given label and constraints. In this paper, the algorithms like naves Bayes, Random Forest and J48 algorithms used to classify the data instances under the given labels using the constraints given., the classification algorithms like naves Bayes shows best performance accuracy with the given student dataset. Clustering and apriori rule have a strong relationship in student performance. In this paper, predictive data mining used to predict the student’s performance to enhance the study level of the students in the organization.
Keywords: Educational Data Mining, Predictive Model, Descriptive Model, Cluster.

Scope of the Article:
Data Mining