Predicting Employability and Admission for MS Students using ML Regression Models

Abstract

Analysing students’ performance concerning their future plans (after under-graduation) is essential in universities, colleges, schools or coaching centres etc. Prospective graduate students always face a dilemma when choosing master’s programs and universities based on their scores (such as GRE, TOEFL, etc.). At the same time, students who opt for jobs as their objective career face a dilemma regarding their employability chances based on their academics, placements and training test scores (such as coding, English, communication etc.). Predicting the candidates’ employability or admission chances based on their scores will guide them to improve their performance. This prediction also helps the faculty improve their teaching skills, provide more resources to the students, and train them most effectively. This paper addresses various machine-learning regression models, such as Gradient Boosting regression, Support Vector Regression, Random Forest regression, Decision Tree Regression, and Ridge Regression. We select the best-performing model, which we will use to indicate whether the university that the MS aspirants are considering is ambitious or safe, and predict the student’s employability chances for their academic placements. This paper also addresses using of streamlit (an open-source app framework) for developing a user-friendly web application interface for users using the best-performing model.