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Classifying wine as "good" is a challenging task due to the absence of aclear criterion. Nevertheless, an accurate prediction of wine quality can bevaluable in the certification phase. Previously, wine quality was evaluatedsolely by human experts, but with the advent of machine learning thisevaluation process can now be automated, thereby reducing the time and effortrequired from experts. The feature selection process can be utilized to examinethe impact of analytical tests on wine quality. If it is established thatspecific input variables have a significant effect on predicting wine quality,this information can be employed to enhance the production process. We studiedthe feature importance, which allowed us to explore various factors that affectthe quality of the wine. The feature importance analysis suggests that alcoholsignificantly impacts wine quality. Furthermore, several machine learningmodels are compared, including Random Forest (RF), Support Vector Machine(SVM), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and Decision Tree(DT). The analysis revealed that SVM excelled above all other models with a96\% accuracy rate.

Wine feature importance and quality prediction: A comparative study of machine learning algorithms with unbalanced data