Predicting Students' Academic Success using Machine Learning Algorithms

This research attempts to predict the academic success of a student using Machine Learning algorithms and a thorough strategy that takes into account a variety of variables that affect students' results. The study examines a large dataset that contains information on the socioeconomic situation, involvement, and academic success of the students. To create predictive models that is able to find out the student who is at an academic risk of quitting and offer timely interventions to enhance their academic performance and prevent dropout, machine learning methods like Classifier based on Random Forest, Classifier Based on Decision Trees, Classifier using Logistic Regression, Voting based Classifier, and others will be used. To enhance students' achievement and raise the standard of education, policy decisions and resource allocation can be made using the knowledge gathered from the analysis.