Quality Prediction of Red Wine based on Different Feature Sets Using Machine Learning Techniques

Nikita Sharma

Abstract: We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeSling consumer tastes from niche markets. In this quality prediction testing is done on the 20 percent of the data and the training is done on the 80 percent of the data. All the results are according to training -0.8 & testing -0.2 Red Wine Dataset.

Keywords: Regression, Classification, Support Vector Classifier, Gradient Boosting Classifiers, Random Forest, K-nn, Na?ve Baye's, Decision Tree