Prediction of wine quality using machine learning techniques

Authors

  • Erestina Gjeka Faculty of Applied Science, University of Donja Gorica

Keywords:

Temperature · Precipitation · Wine · Pearson correlation coefficient.

Abstract

Climate change has affected every sector of nature, especially healthcare in recent years. These changes have affected the vineyards but also the characteristics of the wine. In this research project, two natural factors were taken into account, temperature and annual precipitation. At times when machine learning had not yet been discovered, each process was very complicated and time-consuming. Therefore, machine learning is a very smart move to get fast and accurate results. Pearson correlation coefficient was used to come to a conclusion. 

References

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[6] https://tradingeconomics.com/italy/temperature (Accessed: 21.03.2023)
[7] Buglas J., A. (2022). An Introduction to Viticulture, Winemaking and Wine: From Vineyard to Wine Glass. Cambridge Scholars Publishing, UK, page 66.
[8] Robinson, J. (2015). The Oxford Companion to Wine, Oxford University Press.
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[10] https://www.thirtyfifty.co.uk/spotlight-climate-change.asp#:~:text= Warmer\%20temperatures\%20mean\%20lower\%20acidity,feel\%20lighter\ %20in\%20the\%20mouth. (Accessed: 01.04.2023)
[11] https://www.superiore.de/ (Accessed: 20.03.2023)

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Published

2024-10-25

Issue

Vol. 6 No. 2 (2024): Bánki Közlemények/Bánki Reports Autumn (2024)

Section

Technical Informatics (Műszaki Informatika)

License

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