01750nas a2200301   4500000000100000000000100001008004100002260001200043653002800055653002100083653001800104653001500122653001800137653002300155653000900178100003100187700002100218700003000239700002800269700003300297700002000330245009200350856009500442300001000537490000600547520088100553022001401434       2018                            d  c09/201810aArtificial Intelligence10aMachine Learning10aEmployability10aEmployment10aRandom Forest10aAcademic Analytics10aOEEU1 aFrancisco García-Peñalvo1 aJuan Cruz-Benito1 aMartín Martín-González1 aAndrea Vázquez-Ingelmo1 aJosé Carlos Sánchez-Prieto1 aRoberto Therón00aProposing a Machine Learning Approach to Analyze and Predict Employment and its Factors  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/02/ijimai_5_2_5_pdf_12552.pdf  a39-450 v53 aThis 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.  a1989-1660