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28 March 2023 The classification of red wine quality based on machine learning techniques
Ming Zeng
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Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125973P (2023) https://doi.org/10.1117/12.2672677
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China
Abstract
The main purpose of this research is to observe the relationships between physicochemical properties (inputs) and sensory (output) variables in the practice of data exploring and analysis. Primarily, this paper investigates the correlation between physicochemical properties with the red wine quality. After fundamental analysis, the classification models of the red wine quality are constructed including KNN, XGB, SVC, and Random Forest models. Based on the evaluation metrics, the Random Forest model has the highest accuracy eventually. It is concluded that the concentrations of sulphates and alcohol have positive influences on red wine quality while lowering the concentration of volatile acidity can also increase the quality. According to the outputs of the models created, the Random Forest produced the best performance according to the accuracy, precision, and f1-score values. The main purpose of this paper is to perform scientific observations of red wine quality based on 11 physicochemical data. These results shed light on the physicochemical that are having a positive correlation with the increment of red wine quality, which provides instrumental suggestions for producing high-quality red wine.
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Ming Zeng "The classification of red wine quality based on machine learning techniques", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973P (28 March 2023); https://doi.org/10.1117/12.2672677
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KEYWORDS
Data modeling
Random forests
Machine learning
Performance modeling
Modeling
Analytical research
Support vector machines
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