Predicting College Dropout Rates using Machine Learning: A Student Success Initiative

A student who departs from a college without completing a mandatory credit course is referred to as a "dropout". College dropout rates are a major global problem, with over 40% of students abandoning their studies before completing their degrees. This can have an impact on an individual's life and well-being of society. Machine learning has the potential to reduce college dropout rates by predicting which students are at risk of dropping out and providing them with early intervention support. We have identified and framed the features of the dataset into Academic, Demographic and Socioeconomic features and the ensembled output from these modules have been combined to predict the final dropout. We have used SVM model to train, evaluate and predict the dropout rates with 91.61% of accuracy.