Citas bibligráficas
Alcántara, B., Morales, L., Sierra, J. (2021). Predicción de demanda de GLP para el parque automotor peruano para el segundo semestre del año 2021 [Trabajo de investigación, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/659110
Alcántara, B., Morales, L., Sierra, J. Predicción de demanda de GLP para el parque automotor peruano para el segundo semestre del año 2021 [Trabajo de investigación]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/659110
@misc{renati/1291772,
title = "Predicción de demanda de GLP para el parque automotor peruano para el segundo semestre del año 2021",
author = "Sierra Sanabria, Jhosselin Briyiht",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
This paper shows the current situation of Liquefied Petroleum Gas (LPG) demand in the Peruvian market with respect to the vehicle fleet during the last 6 years. The general objective is to predict the LPG demand for the second semester of the year 2021, through the most relevant variables to know if the local production plus the import of this type of fuel (LPG) will be enough to cover the demand of the automotive sector. The methodology used by the data science team is Cross Industry Standard Process for Data Mining (CRISP-DM), which consists of following a series of ten stages, in each of which the variables that will be relevant for the elaboration of the desired model will be discovered and analyzed. The model selected by the data science team is the predictive learning model because it groups several statistical modeling techniques, including machine learning algorithms. Subsequently, the tools to be used for a better analysis and understanding of the problem will be Power BI, KNime and Python.
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