Online education is becoming increasingly necessary and in high demand as
a result of the current circumstances and the enormous expansion in internet users.
Various studies have been done in this area to enhance the positive benefits of offering
educational courses online. One of the most crucial concerns for learning contexts like
schools and universities, especially during current epidemic period, is the prediction
and analysis of students' performance since it aids in the development of practical
mechanisms that enhance academic achievement and prevent dropout. Most
educational institutions now place a high priority on forecasting and analysing student
performance. That is necessary to assist at-risk students, ensure their retention, provide
top-notch learning tools and opportunities, and enhance the university's ranking and
reputation. This project aims to collect information related to online education and use
Machine Learning to predict students’ performance.
Keywords: Classification algorithms, Decision tree, Machine learning, Naive bayes, Prediction, Random forest, Support vector machine algorithm, WEKA tool.
