Use of decision trees to detect if a room is occupied using Python
Abstract
This article presents a description of the decision trees for determining whether a room is occupied or not. In this research it is empirically demonstrated that it is possible to determine whether a room is occupied or not, using the variables temperature, humidity, luminosity, CO2 level and the humidity ratio, by using decision trees with the SKLEARN libraries in the language Python.
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References
M. Minsky, «The age of Intelligent Machines: Thoughts About Artificial Intelligence,» KurzweilAl.net., 1990.
J. De Andres Oviedo, «Tecnicas de inteligencia artificial aplicadas al analsis de la solvencia empresarial,» Universidad de Oviedo, Oviedo, 2000.
G. R. Solarte Martinez y J. Soto Mejia, «Árboles de decisiones en el diagnóstico de enfermedades cardiovasculares,» Scientia et Technica, vol. XVI, nº 49, pp. 104-109, 2011.
- Conceptualization
- Data curation
- Formal Analysis
- Investigation
- Methodology
- Software
- Validation
- Writing - original draft
- Data curation
- Formal Analysis
- Investigation
- Methodology
- Software
- Validation
- Writing - original draft
- Conceptualization
- Data curation
- Investigation
- Methodology
- Software
- Validation
- Writing - original draft
- Writing - review & editing
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