Abstract
Focusing on the fact that there are deep intricacies involved in a wine's quality and the possibility of having predictive analytics, the current study reviews the effectiveness of various machine learning models at predicting the quality of red wines. Using a wine dataset that includes pleasure of taste, sugar content, average total alcohol, and different parameters, we optimize the data through the use of preprocessing techniques including feature selection and normalization. The choice of a Random Forest Classifier, deemed recognizable for its efficiency and accuracy when dealing with the complexity and multidimensionality of the data, is one of the key components of the methodology we propose. Our study elicits a considerable concern for sciences and future prediction, offering very keen answers as to the primary factors that drive the ranks of wine. Through this study not only the role of machine learning is being enriched, but also the fundamental basis for the foreseeing of beverage quality appears to be established which can be used as a model for the following research work. Thereby, this research work would have a multiplex of impacts on the fields of enology and computational analytics by offering an example for the implementation of the modern machine learning algorithms with the existing approaches to the evaluation of the wine quality.
JEL Code: Y90