Students' Flexibility in Online Education Using Machine Learning

Since COVID-19 spread over the world, “online education” has become a more common phrase. Most schools have moved their activities online even though it took underdeveloped countries like Bangladesh a long time to establish fully online education at all levels. Students also faced difficulties when first introduced to online learning. Decision-makers at educational institutions need to understand the efficacy of online education so that they may take steps to enhance it for students. The aim of this paper is to explore the elements that predict a student's level of adaptability in online learning and contribute to the flexibility of pupils. A dataset downloaded from Kaggle website will be used for this paper. The dataset will be processed, described, visualized, trained, tested and evaluated. This research examines online education's benefits and drawbacks for students. Online education affects students' schedules, learning styles, and academic experience, and technology facilitates remote study. The report also investigates how online education can help geographically or financially disadvantaged students access higher education. The report concludes that online education offers flexibility but also poses unique obstacles that must be considered and adapted to achieve success.