Efficient College Students Higher Education Prediction Using Machine Learning Approaches

Nowadays many students get enrolled in schools and colleges for their academic career. Early identification of students at danger level, alongside precautionary measures, can completely work on their richness. Recently, ML methods have been widely utilized in the education domain to forecast the performance of students. Predicting higher education rates using machine learning can be approached in several ways, based on the existing data and the definite factors being considered. In this paper, pre-processing, selecting features, reformulating the problem, learning the model, predicting performance, and analyzing results has been used as major steps. SVM, RF, and CNN approaches are applied to prognosis the performance of the learners. The suggested model is designed using Python software and the accuracy of the models is compared. Among the three models, CNN can produce a better result by giving accuracy of about 90.75% and Precision and Recall of about 0.90 and 0.88. Predicting higher education rates using machine learning can provide valuable insights into future trends and help stakeholders.