Student Career Prediction Using Machine Learning Approaches

VidyaShreeram, N. and Muthukumaravel, Dr. A. (2021) Student Career Prediction Using Machine Learning Approaches. In: I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India.

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Abstract

India is blessed with the number of good schools and colleges. But most of the students are dropping their next level of education because of various reasons. The reason is many and more, some of the students have some economic problem with their family, some of the students don’t have interest towards their next level of education, some matters about the gender and some rural areas don’t have good schools and educators. So this proposed method deals weather the students will be going to the next level of higher education. This can be evaluated with the concepts of machine learning which the subset of artificial intelligence. Machine learning is made up with the Mathematics and Science concepts. This paper deals with the students’ career prediction by using various machine learning concepts like Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and Adaboost. RF classifier yields better accuracy of 93% compared with other machine learning classifier. Machine learning classifiers are implemented by using Python programming language.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: machine learning classifier support vector machine random forest decision tree adaboost
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 21 Jun 2021 08:11
Last Modified: 21 Jun 2021 08:11
URI: https://eprints.eudl.eu/id/eprint/3911

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