| Citation Request: | |
| 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 | |
| 1. Title: Wine Quality | |
| 2. Sources | |
| Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009 | |
| 3. Past Usage: | |
| 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. | |
| In the above reference, two datasets were created, using red and white wine samples. | |
| 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 importances of the input variables (as measured by a sensitivity | |
| analysis procedure). | |
| 4. Relevant Information: | |
| The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. | |
| For more details, consult: http://www.vinhoverde.pt/en/ or the reference [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 munch 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. | |
| 5. Number of Instances: red wine - 1599; white wine - 4898. | |
| 6. 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. | |
| 7. 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): | |
| 12 - quality (score between 0 and 10) | |
| 8. Missing Attribute Values: None | |