Student Performance Prediction using AI and ML

Abstract

Abstract: As we all know that due to this pandemic situation many problems had been faced in the education stream. As we saw that the most of the students got good marks and on the other hand most students got lesser or average marks. In Critical case the good students got average marks or lesser then there expectations. As a result, all students got the admission but if the person is not that capable but taking admission to that college due to good result .So it seems unfair so to resolve this loophole, we decided to build a software for student performance prediction. It is an important desire in most of the educational departments and institutes to predict the student’s performance. Machine Learning algorithm and descriptive datasets include school, Sex, Age, Address, Family size, P status, M education, F education, M job, F job, Reason, Guardians, Travel time, Study time, Failures, School sup, Family sup, Activities, Free time, Go out, Health, attendance. We have been considered that the prediction and classification of student performance respectively using three type of machine learning algorithms such as Random Forest, ANN, XG Boost are implemented to predict the student’s academic performance. The prediction based models are created to predict academic outcomes of student performance at the end of the year each. The analysis of demographical attributes release that they are also potential indicators of a student's academic success or failure. The results of these case study gives techniques for accurately predicting student performance, and compare the accuracy with MI algorithms Index Terms- ML and AI, ANN, Random Forest, XG Boost prediction model. Index Terms- ML and AI, ANN, Random Forest, XG Boost prediction model. Keywords: Machine Learning, XG-BOOST, Random ForestAlgorithm , Artificial Neural Network, Social Media Post.