Prediction and Analysis of Student Performance using Hybrid Model of Multilayer Perceptron and Random Forest

Educational Data Research lays emphasis on analysis, prediction and generating accurate result. Every educational institute around the globe maintains the denouement repository. These repositories can be lay bared through data mining. In this paper multilayer perceptron and decision tree are set by side to predict the student grades from the repositories in which MLP (Multilayer Perceptron) gave finer and meticulous results compared to decision tree and there after the grades are processed using Random forest to predict the topic of their weak portion and help them to improve their future grades. This model will be helpful for teacher, student and their parents to know in advance about student predicted grade and will enable them to take preventive measure.