wehub-resource-sync
54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
Sebastian Raschka, 2015
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# Python Machine Learning - Supplementary Datasets
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## Wine Dataset
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- Used in chapters 4 and 5
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The Wine dataset for classification.
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|----------------------------|----------------|
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| Samples | 178 |
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| Features | 13 |
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| Classes | 3 |
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| Data Set Characteristics: | Multivariate |
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| Attribute Characteristics: | Integer, Real |
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| Associated Tasks: | Classification |
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| Missing Values | None |
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| column| attribute |
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|-----|------------------------------|
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| 1) | Class Label |
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| 2) | Alcohol |
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| 3) | Malic acid |
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| 4) | Ash |
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| 5) | Alcalinity of ash |
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| 6) | Magnesium |
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| 7) | Total phenols |
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| 8) | Flavanoids |
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| 9) | Nonflavanoid phenols |
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| 10) | Proanthocyanins |
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| 11) | intensity |
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| 12) | Hue |
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| 13) | OD280/OD315 of diluted wines |
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| 14) | Proline |
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| class | samples |
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|-------|----|
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| 0 | 59 |
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| 1 | 71 |
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| 2 | 48 |
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### References
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- Forina, M. et al, PARVUS -
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An Extendible Package for Data Exploration, Classification and Correlation.
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Institute of Pharmaceutical and Food Analysis and Technologies, Via Brigata Salerno,
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16147 Genoa, Italy.
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- Source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)
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- Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
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