International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-1, May 2019 2414 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: A1961058119/19©BEIESP Abstract: Educational Data Mining (EDM) explains the exploration involved with the application of data mining, machine learning and statistical analysis to the enormous amount of data generated from educational institutions. At a high level, the sector seeks to develop and improve strategies for exploring this information, which frequently has multiple levels of significant hierarchy, so as to find new insights regarding the learning process of individuals in the context of such settings. Therefore, the EDM has contributed to theories of learning investigated by researchers in educational psychological science and the learning methodology. This sector is closely tied with the learning analytics, which are compared and contrasted. This work is a comparative analysis of various decision tree classification algorithms using Engineering students’ academic performance data. Educational Data Mining is the process which extracts knowledge through interesting patterns recognized from large amount of data from educational field. Learning related datasets with the performance of students obtained from educational institutions and processed before actual data mining or data analytics process. Data mining is one of the information discovering regions which is broadly used in the field of computer science. Furthermore is an inter-disciplinary area which has great impact on various other fields such as data analytics in prediction of risk factors in business organizations, medical forecasting and diagnosis, market basket analysis, statistical analysis and forecasting, predictive analysis in various other fields. Data mining has multiple forms such as text data mining, web data mining, visual data mining, spatial data mining and Educational data mining. As educational institutions is the source of generating quality students in order to tune them to become an eminent personality. All the educational institutions must be aware of the competency and academic level of every student in order to upgrade their performance. The implementation work is performed in Weka tool to compare the performance accuracy between the different types of decision tree classification algorithms namely J48, Entree and Enhanced Random Tree. These three classifier algorithms which are widely working with the Weka tool are used to classify this learning dataset and the result are obtained and has been evaluated & compared to identify the best decision tree classifier among them. Index Terms: Educational Data mining; Weka, decision tree, classifier, Learning dataset, J48, RepTree, Enhanced Random Tree. Revised Manuscript Received on May 31, 2019 Dr. A.S.ARUNACHALAM, Department of Computer Science, School of Computing Science, VISTAS, Chennai, Tamil Nadu, India. Mr. A. THIRUMURTHI RAJA, Department of Computer Science, School of Computing Science, VISTAS, Chennai, Tamil Nadu, India. Dr. S.PERUMAL, , Department of Computer Science, School of Computing Science, VISTAS, Chennai, Tamil Nadu, India. I. INTRODUCTION Educational Data Mining is an imminent trend in the higher education. The quality of the student’s utilization and enhancement of variety of learning techniques used in Educational Institutions may be improved through discovering hidden knowledge from the large amount of data stored in student databases/ data warehouses. In general the process of data mining has many tasks from pre-processing. The actual task of data mining starts after the pre-processing task obtained raw data in to a processed one so as to apply the data mining techniques [1]. Sturdy patterns if found can doubtless generalize to create correct predictions on future knowledge. The amount of data in the medical field has increased tremendously. Although, such a large volume of information is valuable and need to be analyzed for further forecast perceive and predict the complexities that may arise in future. Data mining gives the methodology and technology to recognize the valuable information of data for higher cognitive process. Machine learning algorithms are widely used to analyses and process various kinds of data [2].There are various tool available to apply the machine learning algorithms to different kinds of data set. These tools allow performing various kinds of tasks from pre-processing till visualization of the obtained result [3]. In this work the Weka tool is used for implementing and evaluating the machine learning algorithms using the medical dataset. II. REVIEW OF LITERATURE Educational data mining is a major area mainly focus for predicting the students’ academic performance in l earning aspects. The extraction of necessary information from collected educational record set and analyzing the information are known as educational data mining. The evaluation of research field and recent improvement in educational filed leads to produce a challenging task in evaluating students’ performance in academics. The necessary steps for educational data mining starts from pre-processing following feature extraction process and ends with analysing stage with necessary clustering and classification algorithms [23]. Data mining techniques are broadly divided into: supervised and unsupervised learning [24,29]. Data mining techniques are pertained to forecast students’ academic performance based on some of the attributes like socioeconomic condition, earlier academic performances and so on. Enhanced Constructive Decision Tree Classification Model for Engineering Students Data A.S. Arunachalam, A. Thirumurthi Raja, S.Perumal