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Proposal for a machine learning-based implementation model for the recruitment of engineering professionals at a public university
Propuesta de un modelo de implementación basado en aprendizaje automático para el reclutamiento de profesionales de ingeniería en una universidad pública
DOI:
https://doi.org/10.15446/dyna.v93n242.124428Palabras clave:
Machine learning, engineering professionals, professional hiring (en)Machine learning, profesionales de ingeniería, contratación de profesionales (es)
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This research aimed to develop and evaluate a machine learning model to optimize the hiring process of engineering professionals at a public university by reducing evaluation time, errors, and subjectivity in CV screening. A quantitative, applied, quasi-experimental approach was used, combining TF-IDF natural language processing, KNN classification with a One-vs-Rest scheme, and experiments on three datasets (10, 20, and 30 CVs). Data from real recruitment processes were processed in Google Colab through cleaning, vectorization, training, and evaluation stages. The model achieved 82% accuracy in the test set, consistently prioritized candidates, and identified academic degree and professional experience as key factors. It reduced average analysis time from 15 to 2.5 minutes per CV and lowered the error rate to below 2%, while standardizing evaluation criteria. The results demonstrate that the model is efficient, objective, scalable, and suitable for institutional implementation.
Esta investigación desarrolló y evaluó un modelo de aprendizaje automático para optimizar la contratación de profesionales de ingeniería en una universidad pública, reduciendo el tiempo de evaluación, los errores y la subjetividad en el análisis de currículums. Se empleó un enfoque cuantitativo, aplicado y cuasiexperimental, utilizando procesamiento de lenguaje natural (TF-IDF), clasificación KNN bajo el esquema One vs-Rest y tres conjuntos de datos de 10, 20 y 30 CV. La información fue procesada en Google Colab mediante etapas de limpieza, vectorización, entrenamiento y evaluación. El modelo alcanzó una precisión del 82 % en la clasificación de candidatos, priorizando de manera consistente a los postulantes según su grado académico y experiencia profesional. Además, redujo el tiempo promedio de evaluación de 15 a 2,5 minutos por CV y disminuyó la tasa de error a menos del 2 %, demostrando ser una herramienta eficiente, objetiva y escalable.
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