Student performance prediction using machine learning algorithm Logistic Regression and KNearest Neighbor Algorithm

Education system is very old and due to a large population of students, there are some serious issues in analyzing and predicting students’ performance. Every institution has its own set of standards for evaluating student success. In existing system there is no proper procedure for monitoring and analyzing a student's performance and progress. In this paper, focus is given on additional external factors like students status, parent occupation etc. That will be more effective in visualizing and analyzing student's performance. This project proposed two types of machine learning algorithms used like K-Nearest Neighbor (KNN) Algorithm and Logistic Regression algorithm. Performance analysis is also done for these two algorithms based on accuracy level of results as well as with some existing work.