Sebastian Raschka, 2015

# Python Machine Learning - Supplementary Datasets

### iris

- used in chapters 1, 2, and 3
- source: [https://archive.ics.uci.edu/ml/datasets/Iris](https://archive.ics.uci.edu/ml/datasets/Iris)

### wine

- used in chapters 4 and 5
- source: [https://archive.ics.uci.edu/ml/datasets/Wine](https://archive.ics.uci.edu/ml/datasets/Wine)

### wdbc

- used in chapter 6
- source: [https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic))

### movie

- used in chapters 8 and 9
- movie dataset converted into a 2-column CSV format: The first column (`review`) contains the text, and the second column (`sentiment`) denotes the polarity, where 0=negative and 1=positive. The first 25,000 are the training samples and the remaining 25,000 rows are the test samples from the "Large Movie Review Dataset v1.0," respectively.
- source: [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)

### housing

- used in chapter 10
- source: [https://archive.ics.uci.edu/ml/datasets/Housing](https://archive.ics.uci.edu/ml/datasets/Housing)

### mnist

- used in chapters 12 and 13
- source: [http://yann.lecun.com/exdb/mnist/]
