wine_quality: Wine Quality

In tyluRp/ucimlr: UCI Machine Learning Repository

Description

This dataset is related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests, see: http://www3.dsi.uminho.pt/pcortez/wine/. 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.).

Usage

wine_quality

Format

A data frame with 6497 observations on the following 13 variables.

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)

Details

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.

Source

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, 2009. '@'2009

References

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, 2009.

https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/

https://archive.ics.uci.edu/ml/datasets/Wine+Quality

http://www3.dsi.uminho.pt/pcortez/wine/

tyluRp/ucimlr documentation built on Feb. 2, 2021, 6:51 a.m.