TY - JOUR KW - Artificial Intelligence KW - Machine Learning KW - Employability KW - Employment KW - Random Forest KW - Academic Analytics KW - OEEU AU - Francisco García-Peñalvo AU - Juan Cruz-Benito AU - Martín Martín-González AU - Andrea Vázquez-Ingelmo AU - José Carlos Sánchez-Prieto AU - Roberto Therón AB - 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. IS - Special Issue on Big Data and Open Education M1 - 2 N2 - 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. PY - 2018 SP - 39 EP - 45 T2 - International Journal of Interactive Multimedia and Artificial Intelligence TI - Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors UR - http://www.ijimai.org/journal/sites/default/files/files/2018/02/ijimai_5_2_5_pdf_12552.pdf VL - 5 SN - 1989-1660 ER -