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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2018
    • vol. 5, nº 2, september 2018
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2018
    • vol. 5, nº 2, september 2018
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    Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors

    Autor: 
    García-Peñalvo, Francisco
    ;
    Cruz-Benito, Juan
    ;
    Martín-González, Martín
    ;
    Vázquez-Ingelmo, Andrea
    ;
    Sánchez-Prieto, José Carlos
    ;
    Therón, Roberto
    Fecha: 
    09/2018
    Palabra clave: 
    artificial intelligence; machine learning; employability; employment; random forest; academic analytics; OEEU; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12369
    DOI: 
    http://doi.org/10.9781/ijimai.2018.02.002
    Dirección web: 
    https://ijimai.org/journal/bibcite/reference/2652
    Open Access
    Resumen:
    This paper presents an original study with the aim of propose and test a machine learning approach to research about employability and employment. To understand how the graduates get employed, researchers propose to build predictive models using machine learning algorithms, extracting after that the most relevant factors that describe the model and employing further analysis techniques like clustering to get deeper insights. To test the proposal, is presented a case study that involves data from the Spanish Observatory for Employability and Employment (OEEU). Using data from this project (information about 3000 students), has been built predictive models that define how these students get a job after finalizing their degrees. The results obtained in this case study are very promising, and encourage authors to refine the process and validate it in further research.
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    Nombre: ijimai_5_2_5_pdf_12552.pdf
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