Student Career Prediction Using Advanced Machine Learning Techniques

  • Authors

    • K Sripath Roy
    • K Roopkanth
    • V Uday Teja
    • V Bhavana
    • J Priyanka
    2018-04-18
    https://doi.org/10.14419/ijet.v7i2.20.11738
  • Student Career Prediction, Decision Tree, Machine Learning, SVM, OneHot Encoder, XGBoost

  • Abstract

    As students are going through their academics and pursuing their interested courses, it is very important for them to assess their capabilities and identify their interests so that they will get to know in which career area their interests and capabilities are going to put them in. This will help them in improving their performance and motivating their interests so that they will be directed towards their targeted career and get settled in that. Also recruiters while recruiting the candidates after assessing them in all different aspects, these kind of career recommender systems help them in deciding in which job role the candidate should be kept in based on his/her performance and other evaluations. This paper mainly concentrates on the career area prediction of computer science domain candidates.

     

     

  • References

    1. [1] P.KaviPriya, “A Review on Predicting Students’ Academic Performance Earlier, Using Data Mining Techniquesâ€, International Journal of Advanced Research in Computer Science and Software Engineering

      [2] Ali Daud, Naif Radi Aljohani, “Predicting Student Performance using Advanced Learning Analyticsâ€, 2017 International World Wide Web Conference Committee (IW3C2).

      [3] Marium-E-Jannat,SaymaSultana,Munira Akther, “A Probabilistic Machine Learning Approach for Eligible Candidate Selectionâ€, International Journal of Computer Applications (0975 – 8887)Volume 144 – No.10, June 2016

      [4] Sudheep Elayidom, Dr. Sumam Mary Idikkula, “Applying Data mining using Statistical Techniques for Career Selectionâ€, International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May 2009.

      [5] Dr. Mahendra Tiwari ,Manmohan Mishra, “Accuracy Estimation of Classification Algorithms with DEMP Modelâ€, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 11, November 2013.

      [6] Ms. Roshani Ade,Dr. P. R. Deshmukh, “An incremental ensemble of classifiers as a technique for prediction of student’s career choiceâ€, 2014 First International Conference on Networks & Soft Computing

      [7] Nikita Gorad ,Ishani Zalte, “Career Counselling Using Data Miningâ€, International Journal of Innovative Research in Computer and Communication Engineering.

      [8] Bo Guo , Rui Zhang, “Predicting Students Performance in Educational Data Miningâ€,2015 International Symposium on Educational Technology

      [9] Ali Daud , Naif Radi Aljohani , “Predicting Student Performance using Advanced Learning Analyticsâ€

      [10] Rutvija Pandya Jayati Pandya , “C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning“, International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 16, May 2015.

      [11] Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest Shiju Sathyadevan and Remya R. Nair

      [12] Yu Lou, Ran Ren, “A Machine Learning Approach for Future Career Planningâ€

      [13] Gareth James ,Daniela Witten ,Trevor Hastie, â€An Introduction to Statistical Learning with Applications in Râ€

      [14] Anuj Karpatne, Gowtham Atluri, “Theory- Guided Data Science: A New Paradigm for Scientific Discovery from Dataâ€, IEEE transactions on knowledge and data engineering, vol.29, no. 10, october 2017.

  • Downloads

  • How to Cite

    Sripath Roy, K., Roopkanth, K., Uday Teja, V., Bhavana, V., & Priyanka, J. (2018). Student Career Prediction Using Advanced Machine Learning Techniques. International Journal of Engineering & Technology, 7(2.20), 26-29. https://doi.org/10.14419/ijet.v7i2.20.11738

    Received date: 2018-04-19

    Accepted date: 2018-04-19

    Published date: 2018-04-18