Wine Quality Analysis using Machine Learning
As the quarantine continues, I’ve picked up a number of hobbies and interests… including WINE. Recently, I’ve acquired a taste for wines, although I don’t really know what makes a good wine. Therefore, I decided to apply some machine learning models to figure out what makes a good quality wine! For this project, I used Kaggle’s Red Wine Quality dataset to build various classification models to predict whether a particular red wine is “good quality” or not. Each wine in this dataset is given a “quality” score between 0 and 10. For the purpose of this project, I converted the output to a binary output where each wine is either “good quality” (a score of 7 or higher) or not (a score below 7). The quality of a wine is determined by 11 input variables: 1)Fixed acidity 2)Volatile acidity 3)Citric acid 4)Residual sugar 5)Chlorides 6)Free sulfur dioxide 7)Total sulfur dioxide 8)Density 9)pH 10)Sulfates 11)Alcohol
The objectives of this project are as follows To experiment with different classification methods to see which yields the highest accuracy To determine which features are the most indicative of a good quality wine