Oracle Machine Learning for Python in APEX - Analyzing and Predicting CO2 Emission by Private Vehicles

Miruna Teleasa () and Alexandra Teodora Bardici ()
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Miruna Teleasa: The Bucharest University of Economic Studies, Romania
Alexandra Teodora Bardici: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2022, vol. 13, issue 1, 47-56

Abstract: Nowadays, the global warming threat is a highly discussed matter. One of the factors that accelerates this process is the air pollution that can be caused by cars' emissions. This paper concerns how the size of the engine, the type of fuel, the fuel consumption, and the transmission type influence the emission of CO2. In order to understand and predict that variable, we used several machine learning algorithms, such as Regression for Generalized Linear Model or K-Means for Hierarchical Cluster Model. The technology that empowered this analysis was Oracle's Machine Learning for Python (OML4Py) that allowed us to integrate both database and data management concepts and data analysis algorithms. By doing that, we managed to discover a pattern for the emission of CO2 based on the factors previously mentioned and, after that, predict future levels of CO2 emissions for various car models.

Keywords: Machine Learning Algorithms; Python; Oracle Autonomous Database; Environment; Regression; K-Means (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:13:y:2022:i:1:p:47-56

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