Author:
Vazaram B. Jhansi,Sankar D. Shiva,Lokesh M.,Mallikarjuna M.
Abstract
The objective of this study aimed to create a model to forecast the quality of red wine by examining its physicochemical attributes. Various factors affect the precision of quality prediction in red wine analysis. This paper presents a computational intelligence approach employing machine learning methods. Specifically, the Random Forest Classifier, Naive Bayes Algorithm, and Support Vector Machine were applied. Using these machine learning techniques and the provided information, it becomes possible to predict the quality of a given red wine sample.
Publisher
International Journal of Innovative Science and Research Technology
Reference10 articles.
1. Paulo Cortez, António Cerdeira, Fernando Almeida, Telmo Matos, &José Reis. Modeling winepreferences by data mining from physicochemical properties. DecisionSupport Systems, 47(4), 547- 553
2. Edelmann, Andrea , et al. "Rapid Method for theDiscrimination of RedWine Cultivars Based on Mid- Infrared Spectroscopy of Phenolic Wine Extracts." Journal of Agricultural & FoodChemistry49.3(2001):1139-1145.
3. Zhang Shiling, Xu Ruimin.Formation and prevention of volatileacidin wine. New Rural Technology,2008 (06): 81-82
4. Dahal, K., Dahal, J., Banjade, H., Gaire, S., 2021. Prediction of Wine Quality Using Machine Learning Algorithms. Open J. Stat. 11, 278– 289
5. Rish, I., 2001. An Empirical Study ofthe Naïve Bayes Classifier. IJCAI 2001 Work Empir Methods Artif Intell 3.
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