Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India

Research Article

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

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  • @INPROCEEDINGS{10.4108/eai.7-6-2021.2308621,
        author={N.  VidyaShreeram and Dr. A.  Muthukumaravel},
        title={Student Career Prediction Using Decision Tree and Random Forest Machine Learning Classifiers},
        proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India},
        publisher={EAI},
        proceedings_a={I3CAC},
        year={2021},
        month={6},
        keywords={random forest machine learning decision tree classifier accuracy career prediction},
        doi={10.4108/eai.7-6-2021.2308621}
    }
    
  • N. VidyaShreeram
    Dr. A. Muthukumaravel
    Year: 2021
    Student Career Prediction Using Decision Tree and Random Forest Machine Learning Classifiers
    I3CAC
    EAI
    DOI: 10.4108/eai.7-6-2021.2308621
N. VidyaShreeram1,*, Dr. A. Muthukumaravel2
  • 1: Research Scholar, Department of Computer Applications, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
  • 2: Dean, Faculty of Arts and Science, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India.
*Contact email: vidushreeram123@gmail.com

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.