Machine Learning Based Graduate Admission Prediction

For academic institutions, choosing graduate applicants for admission is an important task. A reliable system for estimating the likelihood of a candidate being accepted into a graduate school is crucial given the fierce competition for limited spots. In this research, a machine learning strategy for forecasting graduate admission is presented. The suggested technique builds a prediction model using machine learning (ML) algorithms like logistic regression (LR), support vector machines (SVM) and random forest classifier (RFC) using data from previous applicants' graduate record examinations (GRE) scores, cumulative grade point average (CGPA), and university rankings. A variety of performance criteria, including recall, accuracy, and precision, are used to evaluate a model. The findings demonstrate that the suggested machine learning model performs better than current methods and offers a solid resource for predicting graduate admission. This study can help educational institutions find qualified applicants for their graduate programmes and enhance the admissions procedure as a whole.