Wine Quality Prediction

School of Computer Science The University of Adelaide

Artificial Intelligence

Wine Quality Prediction with Decision Tree

Wine experts evaluate the quality of wine based on sensory data. We could also collect the features of wine from objective tests, thus the objective features could be used to predict the expert’s judgement, which is the quality rating of the wine. This could be formed as a supervised learning problem with the objective features as the data features and wine quality rating as the data labels. In this assignment, we provide objective features obtained from physicochemical statistics for each white wine sample and its corresponding rating provided by wine experts. You are expect to implement decision tree learning (DTL), and use the training set to train your decision tree, then provide wine quality prediction on the test set.

Wine quality rating is measured in the range of 0-9. In our dataset, we only keep the samples for quality ratings 5, 6 and 7. The 11 objective features are listed as follows [1]:

f acid: fixed acidity
v acid: volatile acidity
c acid: citric acid
res sugar: residual sugar
chlorides: chlorides
fs dioxide: free sulfur dioxide ts dioxide: total sulfur dioxide density: density
pH: pH
sulphates: sulphates
alcohol: alcohol

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