A statistical analysis of the relationship of civil construction GDP to cement production in Brazil

• Autor(a) principal: Souza, Ana Carolina Rodrigues da Rocha
• Data de Publicação: 2022
• Outros Autores: Gomes, Helton Cristiano, Guimarães, Irce Fernandes Gomes
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/29902
• topic: Construction; Cement production; GDP; Data science; Machine Learning.; Construcción Civil; Producción de Cemento; PIB; Ciencia de Datos; Aprendizaje Automático.; Construção Civil; Produção de Cimento; PIB; Ciência de Dados; Machine Learning.

The ICC plays an important role in the Brazilian economy. This participation in the country's GDP remains, on average, above 5% per year. Cement, one of the main resources in this context, is used in almost all types of constructions in the country. The Brazil are among the 10 largest producers in the world and cement be the main component of concrete, makes widely used. The generation of different data is the starting point for decisions, optimization and forecasting of the activities of this communication network. To transform this data into information, many institutions use with tools such as Data Science. In this sense, this article presents the result of analysis of the behavior of cement production in Brazil, based on results generated through Machine Learning. Trends and seasonality periods were identified, as well as prediction models for future periods were proposed. Verified the existence of a strong positive correlation between cement production and the ICC GDP in Brazil. Machine Learning models were proposed and compared to predict the ICC GDP based on the annual cement production in Brazil, which showed high accuracy. It was concluded that the Ensemble Learning methods adapted better to the data, especially Random Forest.