Comparative Analysis of Baseline Models, Ensemble Models, and Deep Models for Prediction of Graduate Admission

Abstract

AbstractAs there is a rise in number of academic institutes all over the world, shortlisting the universities to apply has become a tedious task for aspiring graduate student. There are many crucial factors involved while submitting the application in any university. The cost involved while applying for any university is one of them. If student profile gets rejected, this may lead to wastage of time and money both. In order to handle this problem, we developed a machine learning-based admission prediction system. This proposed prediction system considers various parameters such as English language score, university rank, statement of purpose, letter of recommendation, cumulative grade point average, and research experience for predicting the chances of admission in a university using ensemble model. Total of 13 machine learning-based prediction models were trained and tested. These 13 models were divided into three categories: baseline models, ensemble models, and deep models. Different performance parameters such as root mean squared error, mean squared logarithmic error, and R2 were used. Execution time for each model was also recorded. In category wise comparison, ensemble models outperformed baseline models and deep models. While in total of 13 models, multiple linear regression and ridge regression outperformed all other models (in terms of high-R2 score, less root mean square error, and less execution time) followed by gradient boosting regression and extra tree regression. KeywordsBaseline modelsDeep modelsEnsemble modelsGraduate admissionPredictionRegression and ridge regression