Abstract
Nowadays, it is extremely difficult to choose wines as there are numerous wine manufacturers. In response to the increase in customer base of wine, wine companies need to improve their quality and sales. There have been many attempts to develop a methodological approach for assessment of wine quality. In this paper, machine learning methods such as decision tree, random forest and support vector are used to check the quality of two types of wine: red and white. This work takes into account various ingredients of wine to predict its quality. The experiments show the superiority of random forest over decision tree and support vector classifiers.
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https://archive.ics.uci.edu/ml/machine-learning-databases/wine/
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Gupta, Y., Saraswat, A. (2023). Wine Quality Prediction Based on Machine Learning Techniques. In: Dwivedi, S., Singh, S., Tiwari, M., Shrivastava, A. (eds) Flexible Electronics for Electric Vehicles. Lecture Notes in Electrical Engineering, vol 863. Springer, Singapore. https://doi.org/10.1007/978-981-19-0588-9_61
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DOI: https://doi.org/10.1007/978-981-19-0588-9_61
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