Sebastian Raschka, 2015

# Python Machine Learning - Supplementary Datasets

## Wine Dataset

- Used in chapters 4 and 5

The Wine dataset for classification.

|				  |		  			|
|----------------------------|----------------|
| Samples                    | 178            |
| Features                   | 13             |
| Classes                    | 3              |
| Data Set Characteristics:  | Multivariate   |
| Attribute Characteristics: | Integer, Real  |
| Associated Tasks:          | Classification |
| Missing Values             | None           |

|	column| attribute	|
|-----|------------------------------|
| 1)  | Class Label                  |
| 2)  | Alcohol                      |
| 3)  | Malic acid                   |
| 4)  | Ash                          |
| 5)  | Alcalinity of ash            |
| 6)  | Magnesium                    |
| 7)  | Total phenols                |
| 8)  | Flavanoids                   |
| 9)  | Nonflavanoid phenols         |
| 10) | Proanthocyanins              |
| 11) | intensity                    |
| 12) | Hue                          |
| 13) | OD280/OD315 of diluted wines |
| 14) | Proline                      |


| class | samples   |
|-------|----|
| 0     | 59 |
| 1     | 71 |
| 2     | 48 |


### References

- Forina, M. et al, PARVUS -
An Extendible Package for Data Exploration, Classification and Correlation.
Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno,
16147 Genoa, Italy.
- Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)
- Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
