Data Mining Techniques for Predicting Employability in Morocco

  • Authors

    • SAOUABI Mohamed
    • EZZATI Abdellah
    2018-12-06
    https://doi.org/10.14419/ijet.v7i4.32.23237
  • Data mining, Big Data, Employability, Classification, Decision tree, Logistic regression, Naïve Bayes.

  • Abstract

    One of the biggest challenges for Big Data applications is to explore large volumes of data and extract valuable information and knowledge for future actions. Employment is the main form of social integration, a factor in improving living conditions and preventing risks of poverty and vulnerability and the most appropriate indicator for assessing the level of social cohesion in a country. Mining employability data will give decision makers a great view of the data and opportunities to make improvement in this sector. In this paper, we presented an experimental study comparing various classification data mining algorithms on employability data in Morocco, which are Decision tree, Logistic regression and Naïve Bayes, which take place in the top 10 data mining algorithms identified by the IEEE International Conference on Data mining. The objective in our experiment is to choose the most efficient and suited algorithm for the employability data.

     


     

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  • How to Cite

    Mohamed, S., & Abdellah, E. (2018). Data Mining Techniques for Predicting Employability in Morocco. International Journal of Engineering & Technology, 7(4.32), 17-20. https://doi.org/10.14419/ijet.v7i4.32.23237

    Received date: 2018-12-06

    Accepted date: 2018-12-06

    Published date: 2018-12-06