Student Career Prediction Using Decision Tree and Random Forest Machine Learning Classifiers

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

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Abstract

Education is so important for the youngsters; some of them don’t have the interest towards schoolings, so they drop their study after certain time. As in this fastest world student are going through their academics and with their interested courses. So this is an important in the entire student to choose his future career. Machine learning approaches are applied in various domains. This proposed work deals with the career predication of the students as weather they will be going for their next level of higher education from their present graduation level using machine learning concepts like DT (Decision Tree) and RF (Random Forest). Applying the concept of DT it yields a result of about 91% of accuracy and applying RF it gives 93% of accuracy level. The result of the proposed system helps the recruiters to select the only needed and proper candidates.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: random forest machine learning decision tree classifier accuracy career prediction
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 08:12
Last Modified: 21 Jun 2021 08:12
URI: https://eprints.eudl.eu/id/eprint/3922

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