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Citation Request:
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This dataset is public available for research. The details are described in [Cortez et al., 2009].
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Please include this citation if you plan to use this database:
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P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
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Modeling wine preferences by data mining from physicochemical properties.
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In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
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Available at: [@Elsevier] http://dx.doi.org/10.1016/j.dss.2009.05.016
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[Pre-press (pdf)] http://www3.dsi.uminho.pt/pcortez/winequality09.pdf
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[bib] http://www3.dsi.uminho.pt/pcortez/dss09.bib
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1. Title: Wine Quality
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2. Sources
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Created by: Paulo Cortez (Univ. Minho), Antonio Cerdeira, Fernando Almeida, Telmo Matos and Jose Reis (CVRVV) @ 2009
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3. Past Usage:
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P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
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Modeling wine preferences by data mining from physicochemical properties.
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In Decision Support Systems, Elsevier, 47(4):547-553. ISSN: 0167-9236.
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In the above reference, two datasets were created, using red and white wine samples.
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The inputs include objective tests (e.g. PH values) and the output is based on sensory data
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(median of at least 3 evaluations made by wine experts). Each expert graded the wine quality
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between 0 (very bad) and 10 (very excellent). Several data mining methods were applied to model
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these datasets under a regression approach. The support vector machine model achieved the
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best results. Several metrics were computed: MAD, confusion matrix for a fixed error tolerance (T),
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etc. Also, we plot the relative importances of the input variables (as measured by a sensitivity
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analysis procedure).
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4. Relevant Information:
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The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine.
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For more details, consult: http://www.vinhoverde.pt/en/ or the reference [Cortez et al., 2009].
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Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables
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are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.).
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These datasets can be viewed as classification or regression tasks.
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The classes are ordered and not balanced (e.g. there are munch more normal wines than
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excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent
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or poor wines. Also, we are not sure if all input variables are relevant. So
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it could be interesting to test feature selection methods.
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5. Number of Instances: red wine - 1599; white wine - 4898.
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6. Number of Attributes: 11 + output attribute
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Note: several of the attributes may be correlated, thus it makes sense to apply some sort of
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feature selection.
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7. Attribute information:
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For more information, read [Cortez et al., 2009].
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Input variables (based on physicochemical tests):
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1 - fixed acidity
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2 - volatile acidity
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3 - citric acid
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4 - residual sugar
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5 - chlorides
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6 - free sulfur dioxide
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7 - total sulfur dioxide
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8 - density
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9 - pH
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10 - sulphates
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11 - alcohol
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Output variable (based on sensory data):
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12 - quality (score between 0 and 10)
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8. Missing Attribute Values: None
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