This dataset is public available for research. The details are described in [Cortez et al., 2009]. Please include this citation if you plan to use this database:

P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.

Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016 [Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf [bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib

The inputs include objective tests (e.g. PH values) and the output is based on sensory data(median of at least 3 evaluations made by wine experts).

Each expert graded the wine quality between 0 (very bad) and 10 (very excellent).

Several data mining methods were applied to model these datasets under a regression approach. The support vector machine model achieved the best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T), etc. Also, we plot the relative importance of the input variables (as measured by a sensitivity analysis procedure).

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.

• Number of Instances: red wine - 1599
• Number of Attributes: 11 + output attribute
• Note: several of the attributes may be correlated, thus it makes sense to apply some sort of feature selection.

Input variables (based on physicochemical tests):

Acidity:

1. fixed acidity (tartaric acid - g / dm^3) 酒石酸
2. volatile acidity (acetic acid - g / dm^3) 醋酸
3. citric acid (g / dm^3) 柠檬酸

Sweetness:

4. residual sugar (g / dm^3) 糖分
5. alcohol (% by volume) 酒精

Saltiness:

5. chlorides (sodium chloride - g / dm^3氯化钠

Bitterness:

6. free sulfur dioxide (mg / dm^3) 游离二氧化硫
7. total sulfur dioxide (mg / dm^3) 总二氧化硫
8. sulphates (potassium sulphate - g / dm3) 硫酸钾

Others:

8. density (g / cm^3)
9. pH

Output variable (based on sensory data):

12. quality (score between 0 and 10)

1. fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily)
2. volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste
3. citric acid: found in small quantities, citric acid can add 'freshness' and flavor to wines
4. residual sugar: the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet
5. chlorides: the amount of salt in the wine
6. free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine
7. total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine
8. density: the density of water is close to that of water depending on the percent alcohol and sugar content
9. pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale
10. sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant
11. alcohol: the percent alcohol content of the wine
12. quality (score between 0 and 10)