Implementación de una aplicación móvil con Machine Learning aplicando la nueva metodología CSKT para el proceso de contratación de personal en el sector privado

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Resumen

This paper evaluates a Machine Learning model developed using the CSKT methodology to improve pension allocation in engineering projects. It addresses the challenges in adopting such models by following a structured approach from understanding the business model to presenting it. The initial decision tree model training phase yields an F1-Score of 0.699825, indicating balanced precision and recall. Individual precision is 0.650974, and recall is 0.756603, demonstrating accurate prediction of positive outcomes. The ROC curve has a value of 0.739770, assessing the discriminatory ability of the model. The results include defining the model parameters, creating a decision tree, and performing initial simulations. The conclusion highlights CSKT's effectiveness in pension allocation and the stresses of ongoing client collaboration in engineering projects. The numerical results highlight the model's contribution to improving pension allocation processes in engineering projects.

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Palabras clave

Machine learningCSKT methodologymobile applicationsrecruitment managementAprendizaje automáticometodología CSKTaplicaciones móvilesgestión de contratación de personal