Abstract
The prediction of wine quality holds great significance for individuals who regularly consume it as part of their health regimen. Wine production companies adhere to specific criteria and standards to ensure both quantity and quality. However, the quality of wine can vary based on factors such as cost and brand, making it challenging for an average person to determine its true quality. In this study, we propose a machine learning-based model designed to predict wine quality. To construct our model, we gathered a comprehensive dataset from the Kaggle website, which provided a diverse range of data classes suitable for wine quality prediction. Our approach involves utilizing classification algorithms, namely logistic regression, decision tree, and random forest. Remarkably, the decision tree algorithm achieved an impressive accuracy rate of 85% when applied to the given dataset, surpassing the performance of other models.
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Sravan, K., Gunakar Rao, L., Ramineni, K., Rachapalli, A., Mohmmad, S. (2024). Analyze the Quality of Wine Based on Machine Learning Approach. In: Nanda, S.J., Yadav, R.P., Gandomi, A.H., Saraswat, M. (eds) Data Science and Applications. ICDSA 2023. Lecture Notes in Networks and Systems, vol 820. Springer, Singapore. https://doi.org/10.1007/978-981-99-7817-5_26
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DOI: https://doi.org/10.1007/978-981-99-7817-5_26
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