3.1 Task Description

For this task, we will use wine quality data set available here: https://archive.ics.uci.edu/ml/datasets/Wine+Quality. There are separate CSV files for white and red wine. Combine them and make a larger united file.

3.1.1 Data Set Information

This part is verbatim reproduced from UCI.

The two data sets are related to red and white variants of the Portuguese “Vinho Verde” wine. For more details, consult the paper by Cortez et al., 2009. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).

These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods.11

Attribute Information: For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests):

  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. sulphates

  11. alcohol

Output variable (based on sensory data):

  1. quality (score between 0 and 10)

  1. We are not doing feature selection in this exercise.↩