Bibliographic citations
Chuquihuamani, K., (2024). Predicción de la rotación de personal y optimización de la estructura de retención en un outsourcing delivery center usando Machine Learning y Programación Multiobjetivo [Pontificia Universidad Católica del Perú]. http://hdl.handle.net/20.500.12404/28315
Chuquihuamani, K., Predicción de la rotación de personal y optimización de la estructura de retención en un outsourcing delivery center usando Machine Learning y Programación Multiobjetivo []. PE: Pontificia Universidad Católica del Perú; 2024. http://hdl.handle.net/20.500.12404/28315
@mastersthesis{renati/530808,
title = "Predicción de la rotación de personal y optimización de la estructura de retención en un outsourcing delivery center usando Machine Learning y Programación Multiobjetivo",
author = "Chuquihuamani Altamirano, Karen Elizabeth",
publisher = "Pontificia Universidad Católica del Perú",
year = "2024"
}
Employee turnover has become a research focus in the Human Resources area because has significant effects on the performance of organizations regardless of geography, company size or sector. Staff turnover affects the company as a business and as a work culture, many managers believe that its effects are easy to measure: the cost incurred in hiring and training new staff, but high staff turnover implies hidden costs such as loss of confidence in the employee, a harmful work environment, in addition to allowing information leaks and little sense of permanence. In this sense, the use of Machine Learning to predict the probability that an employee will quit their job could greatly increase the ability of the Human Resources department to intervene in time and provide a mitigating approach to this situation. This study is carried out to compare the performance of machine learning techniques, including XGBoosting, decision tree, random forest, KNN, SVM, logistic regression, and LGBM, to select the best model that seeks to predict the retention of candidates in their first year of work in technical, planning, and strategic positions in a Peruvian outsourcing company. All of this will be done under the open standard CRISP-DM model, a proven method for guiding data mining work. Finally, taking as input the results of the selected classifier, a bi-objective nonlinear optimization model will be built to minimize costs after applying retention strategies and reduce the wage gap between the current salary and the market salary, aiming to minimize the attrition rate. The study will help management to adopt retention techniques based on the attributes that most impact an employee's decision to voluntarily resign from a company.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.