Wine Dataset

The data was used with many others for comparing various classifiers. In a classification context, this is a well posed problem with "well behaved" class structures. A good data set for first testing of a new classifier, but not very challenging.

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars.
The analysis determined the quantities of 13 constituents found in each of the three types of wines.

The attributes are:

• Alcohol
• Malic acid
• Ash
• Alcalinity of ash
• Magnesium
• Total phenols
• Flavanoids
• Nonflavanoid phenols
• Proanthocyanins
• Color intensity
• Hue
• OD280/OD315 of diluted wines
• Proline

For Each Attribute: All attributes are continuous

No statistics available, but suggest to standardise
variables for certain uses (e.g. for us with classifiers
which are NOT scale invariant)

NOTE: 1st attribute is class identifier (target)(1-3)

Acknowledgements:
This dataset is also available from Kaggle & UCI machine learning repository, https://archive.ics.uci.edu/dataset/109/wine