Student Academic Performance Using Machine Learning

Education is an issue that is too big and important to be stressed enough. In the past few years, there has been a substantial surge in the volume of data stored in education databases. These databases house sensitive information pertaining to enhancing students' academic achievements. The capacity to forecast students' educational performance holds immense significance in the field of education. The education of students in a school is based on mental and environmental factors and is estimated by different educational data mining techniques such as logistic regression, multilayer perceptron, K nearest neighbor, decision tree, Naive Bayes algorithm. With this, teachers can provide feedback based on students' needs. The dataset handed-down is xAPI-EDU-DATA.