Students Performance Prediction Using Data Mining Techniques

Data Mining is a rising field utilized in educational purposes to enhance the insightful and learning strategy for students. It centers around perceiving, extricating and calculating information related to the educational procedure and rising student performance. Mining in a learning field is known as educational data mining with investigating most recent strategies to search out data from instructional fields. The aim behind our study is to judge the students performance by taking different parameters like scholastic accomplishments (CGPA), sex, class test grade, condition of class, Fund/Scholarships/Private and so on. In our exploration we will utilize classification and clustering Bayesian classification-mean algorithms, neural networks, Naive bayes, Web based system and nearest neighbor methods strategies to investigate understudy execution. The issue is to distinguish the student whose execution is poor in their courses. So as indicated by their execution and capacity, there will be an opportunity to take some remedial activities. In this paper, we anticipate the last grades utilizing diverse information mining calculations to foresee the last execution of students with the goal that we can get increasingly precise qualities. It issues a prediction for each student solely when the normal precision of the forecast is satisfactory.