Red Wine Quality Analysis based on Machine Learning Techniques

This is the authors' abstract. We don't add key points for this paper.

The red wine industry is growing at a tremendous speed as more and more people start to drink wine. Therefore, the industry is becoming competitive and wine companies need to make better quality wines to stand out. This paper used machine learning techniques to analyze 1599 wine samples each with 11 input variables in order to find the variables that have the most impact on wine's general quality. The linear regression model used in the paper shows the most influential variables on quality are alcohol and acid. In addition, a heat map was adopted to show all the correlation between the variables. To go deeper, box plot and 3D scatter plot were used to support the finding through linear regression model and have a more detailed conclusion on the variables that have the most impact on quality. These results shed light on what are the most influential variables on wine’s quality.