Red Wine Classification (Python)

From the last, we will continue with the wine dataset. Here, I will apply machine learning technique to classify it. Just to remember, we have 3 categories: low, medium and high.

I used five algorithms from scikit learn package: KNeighbors, Random Forest, Gaussian NB, ExtraTrees and DecisionTree. Here is the accuracy

The random forest gives the highest accuracy around 88% and we can have also the most important predictors.

The most important features for red wine classification are : alcohol, volatile acidity, sulphates, density and total sulfur dioxide. Indeed, these are important parameters to evaluate the quality of a wine.