Modelos de predicción de renuncia de colaboradores en el Banco de Bogotá

Abstract

This report, titled “Prediction Models for Employee Resignation at Banco de Bogotá,” aims to present the work completed during my internship at the company. The primary objective is to develop and implement models to predict potential employee resignations among active staff members. To achieve this, two models were utilized: Random Forests and Logistic Regression. For the latter, regularization techniques such as Lasso, Ridge, and Elastic Net were employed to create heat maps for detecting potential talent attrition. Additionally, the most influential variables were identified, providing valuable insights that will help the company take concrete actions, such as implementing employee retention projects, ultimately aiding in the preservation and management of human talent.