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https://github.com/hfagerlund/machine-learning-iris-analysis

No longer maintained. Moved to https://github.com/hfagerlund/machine-learning-classifier-iris/.
https://github.com/hfagerlund/machine-learning-iris-analysis

data-visualization jupyter-notebook machine-learning python37

Last synced: 8 months ago
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No longer maintained. Moved to https://github.com/hfagerlund/machine-learning-classifier-iris/.

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# machine-learning-iris-analysis

## No longer maintained. Moved to [`machine-learning-classifier-iris`](https://github.com/hfagerlund/machine-learning-classifier-iris/).

A machine learning classifier for identifying/predicting the type of iris (ie. setosa, versicolor, or virginica) based on its (petal, sepal) features.

## Features

Data is:
* loaded
* described
* visualized (somewhat)
* split into 'train' and 'test' sets

Then:
* (2) machine learning models (ie. classifiers; supervised learning algorithms) are created;
* the models are 'fit' to the training data;
* (class) predictions are made for new/out-of-sample/test data;
* the accuracy of the algorithms is evaluated and compared.

## Requirements

* Python v3.7.0
* Jupyter Notebook server v5.6.0
* IPython v6.5.0
* Iris flowers dataset (included with [scikit-learn](https://github.com/scikit-learn/scikit-learn))

(All copyrights for the above remain with their respective owners.)

## License
Copyright (c) 2018 Heini Fagerlund. Licensed under the [MIT License](https://github.com/hfagerlund/machine-learning-iris-analysis/blob/master/LICENSE).