Predicting Employability and Admission for MS Students using ML Regression Models

G. S. Krishna Kireeti, J. Prithvi, Mangala Divya, C. Kumari
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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.
用ML回归模型预测MS学生的就业能力和录取
分析学生的表现与他们的未来计划(本科毕业后)是必不可少的大学,学院,学校或辅导中心等。未来的研究生在根据自己的成绩(如GRE、托福等)选择硕士项目和大学时,总是面临着一个两难的境地。与此同时,选择工作作为目标职业的学生面临着基于他们的学术,实习和培训考试成绩(如编码,英语,沟通等)的就业机会的困境。根据考生的分数预测他们的就业能力或录取机会,将指导他们提高自己的表现。这种预测也有助于教师提高他们的教学技能,为学生提供更多的资源,并最有效地培训他们。本文讨论了各种机器学习回归模型,如梯度增强回归、支持向量回归、随机森林回归、决策树回归和岭回归。我们选择表现最好的模型,我们将用它来表明有硕士抱负的人正在考虑的大学是雄心勃勃还是安全的,并预测学生在学术实习中的就业机会。本文还讨论了使用streamlit(一个开源应用程序框架)为使用最佳性能模型的用户开发用户友好的web应用程序界面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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