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Prediction of the Disappearance of Companies From the Market in Bogotá, Colombia Using Machine Learning
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Author(s): William Stive Fajardo-Moreno (EAN University, Colombia), Rubén Dario Acosta Velásquez (EAN University, Colombia), Ivan Dario Castaño Pérez (Ruta N, Colombia)and Leonardo Espinosa-Leal (Arcada University of Applied Sciences, Finland)
Copyright: 2021
Pages: 20
Source title:
Handbook of Research on Management Techniques and Sustainability Strategies for Handling Disruptive Situations in Corporate Settings
Source Author(s)/Editor(s): Rafael Perez-Uribe (Universidad de la Salle, Colombia), David Ocampo-Guzman (EAN University, Colombia), Nelson Antonio Moreno-Monsalve (EAN University, Colombia)and William Stive Fajardo-Moreno (EAN University, Colombia)
DOI: 10.4018/978-1-7998-8185-8.ch011
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Abstract
In this chapter, the results concerning the modeling of companies' disappearance from Bogota's market using machine learning methods are presented. The authors use the available information from Bogota's Chamber of Commerce, where the companies are registered yearly. The dataset comprises the years 2017 to 2020 with almost 3 million registries. In this work, a deep analysis of the different features of the data is presented and explained. Next, four state-of-the-art machine learning models are trained for comparison: logistic regression (LR), extreme learning machine (ELM), random forest (RF), and extreme gradient boosting (XGBoost), all with five-fold cross-validation and 50 steps in the randomized grid search. All methods showed excellent performance, with an average of 0.895 in the area under the curve (AUC), being the latter algorithm the best overall (0.97). These results are in agreement with the state-of-the-art values in the field and will be of paramount importance to assess companies' stability for Bogota's local economy.
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