chore: import upstream snapshot with attribution

2026-07-13 13:38:23 +08:00
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+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.