Chapter 48 A Machine Learning Approach to Analyze and Predict the Factors in Education System: Case Study of India Jeena A. Thankachan and Bama Srinivasan 1 Introduction Education is the soul of any country for the sake of its development and progress. Pros- perity and country building is possible only when a nation has a solid and successful education approach and policy. The literacy rate of India was 12.2% in 1947 which has increased to 74.0% in 2011 census. Although it looks like an accomplishment, still many are there without access to education. The literacy rates in the states of India are dependent on the number of time variants and time-invariant attributes. According to study by KPMG (one of the Big Four accounting organizations) online education market in India is expected to grow to billion from million dollars by 2021 and from million users in 2016 to billion users in 2021. But there are many achievement gaps to be met before achieving the target [1]. Those achievement gaps include availability of digital learning solutions and learning software for teachers to conduct online classes, learning models for effective delivery of education, and other integration of technology methods in the existing Indian education system [2]. In addition to these gaps, Covid-19 pandemic has also drastically affected the education sector as well. Based on the influence of pandemic Covid-19 on education, UNESCO has compiled data globally that lead to the closures of schools and colleges in the quarter-1 of the year 2020 on the scale of localized and national level. The visualization map presented (see Fig. 1), obtained from the dataset [3] analyzed on tableau shows the school closures with note on initiation of distance learning, home- based learning, and online learning for upcoming academic year 2020–21. Initiation J. A. Thankachan (B ) · B. Srinivasan Department of Information Science and Technology, Anna University, CEG Campus, Chennai 600025, India B. Srinivasan e-mail: bama@auist.net © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 S. Kumar et al. (eds.), Proceedings of International Conference on Communication and Computational Technologies, Algorithms for Intelligent Systems, https://doi.org/10.1007/978-981-16-3246-4_48 619