GitHub repository: KaiyeYang-git/A-Method-of-Wine-Classification
Primary language: Jupyter Notebook
Created: 2022-05-24T11:57:52Z
Description: Owing to technological limitation, human experts of wine knowledge are the only eligible measurements for commenting and evaluating wine taste quality since ancient time. With the development of physicochemical measurement instruments, scientists are able to capture diverse wine attributes from the sample drops, which triggers them to rethink about the connections among these extracted features and the benchmarks inherited from ancestors. In the following paragraphs, different machine learningbased methods are applied on trying to understand the relationship between taste quality of wine and its corresponding data, background by a Portuguese wine speciality. Since this type of wine speciality are made by two distinctive grapes, the measured data are significantly different from each other, so the question of taste quality class will occur after the classification of wine types, which generates some calculated errors varied from model’s performance. Fortunately, random forest model received the highest accuracy rate in both type and quality classification at 78%, showing that almost three quarters of unmarked wine would be known their taste before removing the corks, which is beneficial for wine manufactory to check the acceptance rate of prepare-to-launch products or make quality prediction in advertisement. Furthermore, it is interesting that the alcohol degree has become the most vital feature on determining the sensory taste of wine, which might be a good recommendation of wine industry to optimise their new products.