Commit Inicial

data/tanveer/wine-quality-red/datos_orixinais/winequality.names

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+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