From Vineyard Data to Flavour Profiles: Machine Learning Predicts Wine Quality

This paper looks at how well three machine learning models can predict wine quality. The models being compared are K Nearest Neighbors, Gradient Boosting, and Extreme Gradient Boosting. They used a public dataset that includes different chemical properties of wine to help make these predictions. To check how well the models worked, we calculated measurements like accuracy, precision, recall, F1-score, and RMSE for each one. The results showed that XGB was more accurate and better at predicting than KNN and GB. This study looks at how advanced boosting methods could help make more accurate predictions for assessing wine quality. These results can help the winemaker or quality checker in the wine industry predict the quality of wine better. Future research could use more factors and different machine learning methods to improve prediction accuracy.