Student Dropout Analysis for School Education

The "Student Dropout Analysis for School Education" initiative focuses on the study of dropout student data in schools. Dropping out of school refers to being absent from school for multiple days without a valid excuse. To address this obstacle, a detailed understanding of the underlying difficulties is required, as well as good intervention planning. In an effort to address the issue of early school departure, machine learning has garnered a lot of interest over the years. This is so that prompt intervention planning and the identification of kids who are at-risk can be facilitated by machine learning approaches. To gather, arrange, and synthesize the body of knowledge already known about machine learning to prevent dropouts, case studies, books, and scientific journals were reviewed. According to the survey, there have been various machine learning algorithms proposed in the literature. But the majority of these algorithms were created and evaluated in industrialized nations. Consequently, there is a dearth of research on applying machine learning to this issue in poor nations. Additionally, a lot of research uses student-level datasets to address college dropout. However, underdeveloped nations must incorporate datasets at the school level due to resource constraints. Thus, an overview of machine learning in education is given in this work, with a particular emphasis on dropout prediction methods.