International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 07 Issue: 05 | May 2020 www.irjet.net p-ISSN: 2395-0072 © 2020, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1081 STUDENT PERFORMANCE PREDICTION USING DATA MINING TECHNIQUES Durgesh Ugale 1 , Jeet Pawar 2 , Sachin Yadav 3 , Dr. Chandrashekhar Raut 4 1 Student, Dept. of Computer Engineering, Datta Meghe College of Engineering, Airoli, Maharashtra, India 2 Student, Dept. of Computer Engineering, Datta Meghe College of Engineering, Airoli, Maharashtra, India 3 Student, Dept. of Computer Engineering, Datta Meghe College of Engineering, Airoli, Maharashtra, India 4 Ph.D Professor, Dept. of Computer Engineering, Datta Meghe College of Engineering, Airoli, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - The success of an academic institution can be measured in terms of quality of education provides to its students. In the education system, highest level of quality is achieved by exploring the data relating to redirection about students performance. These days the lack of existing system to analyse and judge the students performance and progress isn‟t being addressed. There are 2 reasons why this is often happening. First, the present system is not accurate to predict students‟ performance. Second, because of shortage of consideration of some vital factor those are affecting students‟ performance. Predicting students‟ performance is more challenging task as a result of large amount of information in academic database. This proposed system can help to predict students‟ performance more accurately. For these suitable data mining approach will be applied. In this approach, preprocessing step will be applied to raw dataset so that the mining algorithm will be applied properly. The prediction about students‟ performance can help him/her to enhance the performance. Key Words: Education, student, performance, data mining, pre-processing, database, prediction 1. INTRODUCTION Improving student’s academic performance is not an easy task for the academic community of higher learning. The academic performance of engineering and science students during their first year at university is a turning point in their educational path and usually encroaches on their General Point Average (GPA) in a decisive manner. The students evaluation factors like class quizzes mid and final exam assignment lab -work are studied. It is recommended that all these correlated information should be conveyed to the class teacher before the conduction of final exam. This study will help the teachers to reduce the drop out ratio to a significant level and improve the performance of students. In this paper, we present a hybrid procedure based on Decision Tree of Data mining method and Data Clustering that enables academicians to predict student‟s GPA (SGPA, CGPA) and based on that instructor can take necessary step to improve student academic performance. Graded Point Average (gpa) is a commonly used indicator of academic performance. Many universities set a minimum gpa that should be maintained. Therefore, gpa still remains the most common factor used by the academic planners to evaluate progression in an academic environment. Many factors could act as barriers to student attaining and maintaining a high gpa that reflects their overall academic performance, during their tenure in university. These factors could be targeted by the faculty members in developing strategies to improve student learning and improve their academic performance by way of monitoring the progression of their performance. With the help of clustering algorithm and decision tree of data mining technique it is possible to discover the key characteristics for future prediction. Data clustering is a process of extracting previously unknown, valid, positional useful and hidden patterns from large data sets. The amount of data stored in educational databases is increasing rapidly. Clustering technique is most widely used technique for future prediction. The main goal of clustering is to partition students into homogeneous groups according to their characteristics and abilities. These applications can help both instructor and student to enhance the education quality. This study makes use of cluster analysis to segment students into groups according to their characteristics. Decision tree analysis is a popular data mining technique that can be used to explain different variables like attendance ratio and grade ratio. Clustering is one of the basic techniques often used in analysing data sets. This study makes use of cluster analysis to segment students in to groups according to their characteristics and use decision tree for making meaningful decision for the student’s. 2. PREVIOUS WORK Data mining (sometimes known as knowledge or information discovery) is the method of analysing information from totally different views and summarizing it into useful information. Information that may be used to increase revenue, cuts costs, or both data mining software system is one of the varieties of analytical tools for analysing information. It permits users to analyse the information identity. Technically, data mining/data processing is the process of finding correlations or patterns among dozens of fields in massive relational databases. Following are the survey papers being studied:  Paris et. al.(1), compared data mining methods accuracy to classifying students in order to predicting category grade of a student. These predictions are more helpful for identifying