Abstract
In recent years, people have been requiring new livelihoods that allow them to have enough economical resources for the development of their daily activities, considering the problematic that COVID-19 has brought to their lives. The objective of this research was to analyze machine learning algorithms such as Decision Tree, Random Forest, Naive Bayes, Logistic Regression and Vector Support Machine, in order to identify the risk level to fall into poverty for a person in Perú, basing the analysis on the National Household Survey (NHS) that the National Institute of Statistics and Informatics (NISI) provided on 2020. The methodology was presented in four stages, organization and structuring of the database, analysis and identification of the variables, application of the learning algorithms and evaluation of the performance of the aforementioned algorithms. Python programming language and the STATA software allowed the exploration of 91,315 registers and 33 variables. Results showed that the Decision Tree algorithm has an accuracy of 98%, while other algorithms are below the indicated range, so dynamism is expected in the application of this algorithm for socioeconomic areas that can be materialized through a permanent evaluation and analysis platform that helps to focus strategies and proposals for the benefit of the population with economic limitations.
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References
National Institute of Statistics and Informatics, Peru: Evolution of Monetary Poverty 2009, 2020 (2021)
Vargas, C.M.: Reflections on COVID-19 infection, Medical College of Perú and the public health. Acta Med. Perú 37, 8–10 (2020). https://doi.org/10.35663/amp.2020.371.929
Mahsa Alavi, S., Omid Mahdi Ebadati, A.E., Masoud Alavi, S.A., Firoozan Sarnaghi, T.: Determination of households benefits from subsidies by using data mining approaches. J. Inform. Technol. Polit. 1 (2022). https://doi.org/10.1080/19331681.2022.2097974
Guaraca, M.E.O., Castro, R., Pallaroso, A.A., Machado, A., Sucozhañay, D.: Machine learning approach for multidimensional poverty estimation. Revista Tecnológica - ESPOL 33(2), 205–225 (2021). https://doi.org/10.37815/rte.v33n2.853
United Nations Children’s Fund: COVID-19: Impact on poverty and inequality in children and adolescents in Perú. Estimations 2020–2021. UNICEF (2020)
Manayay, D.T.: El empleo informal en el Perú: Una breve caracterización 2007–2018. Pensamiento Crítico 25(1), 51–75 (2020). https://doi.org/10.15381/pc.v25i1.18477
Gamero, J., Pérez, J.: Perú > Impact of COVID-19 on employment and labor income. Organización Internacional de Trabajo 1 (2020). https://www.ilo.org/wcmsp5/groups/public/---americas/---ro-lima/documents/publication/wcms_756474.pdf
Barreto, I.B., Sánchez, R.M.S., Marchan, H.A.S.: Consecuencias económicas y sociales de la inamovilidad humana bajo Covid – 19 caso de estudio Perú. Lecturas de Economía 94, 285–303 (2021). https://doi.org/10.17533/udea.le.n94a344397
Fernández, A.: I Artificial intelligence in financial services. Analytical articles. Econ. Bull. Scielo. 5, 10 (2019)
Leal, F., Molina, C., Zilberman, E.: Projection of Inflation in Chile with Machine Learning Methods. Chile Central Bank (2020)
Lora, E.: Forecasting formal employment in cities. Econ. Bull. Rosario. 24, 1–38 (2021). https://doi.org/10.12804/revistas.urosario.edu.co/economia/a.10029
Aceituno Rojo, M.R.: Predictive model of credit risk analysis using Machine Learning in an entity of the microfinance sector. UNA-Puno, vol. 102 (2019)
Raschka, S., Mirjalili, V.: A tour of machine learning classifiers with scikit-learn. In: Python Machine Learning, pp. 73–127. Spain (2019)
Géron, A.: Decision tree. In: Learn Machine Learning with Scikit-Learn, Keras and TensorFlow, p. 197 (2019)
Ávila-Toscano, J.H., Pérez, I.R., Guajardo, E.S., Marenco-Escuderos, A.: Influencia de la producción de nuevo conocimiento y tesis de postgrado en la categorización de los grupos de investigación en Ciencias Sociales: árbol de decisiones aplicado al modelo científico colombiano. Revista española de Documentación Científica 41(4), 218 (2018). https://doi.org/10.3989/redc.2018.4.1547
Géron, A.: Random forest. In: Learn Machine Learning wih Scikit-Learn, Keras and TensorFlow, p. 216 (2019)
Quintana-Zaez, I.J.C., Velarde-Bedregal, H.R., Anton-Vargas, J., Joaquim-Luis, G.: Schemes of combination of decision trees as a strategy for anomaly detection. In: Proceedings of the LACCEI International Multi-Conference Engineering Education Technology (2020). https://doi.org/10.18687/LACCEI2020.1.1.306
Mosquera, R., Castrillón, O.D., Parra, L.: Support vector machines, Naïve Bayes classifier and genetic algorithms for the prediction of psychosocial risks in teachers of Colombian public schools. Inf. Tecnol. 29, 153–162 (2018). https://doi.org/10.4067/S0718-07642018000600153
Godoy Viera, A.F.: Machine learning techniques used for text mining. Bibl. Res. 31, 103–126 (2017). https://doi.org/10.22201/IIBI.0187358XP.2017.71.57812
Bagnato, J.I.: Logistic regression. In: Learn Machine Learning in Spanish - Theory + Practice Python. p. 43. España (2020)
Zhang, D.: Support vector machine. In: Fundamentals of Image Data Mining. TCS, pp. 179–205. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-17989-2_8
Kanani, P., Padole, M.: Deep learning to detect skin cancer using google colab. Int. J. Eng. Adv. Technol. 8(6), 2176–2183 (2019). https://doi.org/10.35940/ijeat.F8587.088619
Bagnato, J.I.: Install the python development environment. In: Learn Machine Learning in Spanish - Theory + Practice Python, p. 7 (2020)
Zárate-Valderrama, J., Bedregal-Alpaca, N., Cornejo-Aparicio, V.: Classification models to recognize patterns of desertion in university students. Ingeniare 29, 168–177 (2021). https://doi.org/10.4067/S0718-33052021000100168
Bagnato, J.I.: Metrics and confusion matrix. In: Learn Machine Learning in Spanish - Theory + Practice Python, pp. 79–82 (2020)
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Silva Marchan, H.A., Peña Cáceres, O.J.M., Ricalde Moran, D.M., Samaniego-Cobo, T., Perez-Espinoza, C.M. (2022). A Machine Learning Study About the Vulnerability Level of Poverty in Perú. In: Valencia-García, R., Bucaram-Leverone, M., Del Cioppo-Morstadt, J., Vera-Lucio, N., Jácome-Murillo, E. (eds) Technologies and Innovation. CITI 2022. Communications in Computer and Information Science, vol 1658. Springer, Cham. https://doi.org/10.1007/978-3-031-19961-5_1
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