https://github.com/hfagerlund/machine-learning-classifier-iris
Algorithm(s) for identifying/predicting type of iris
https://github.com/hfagerlund/machine-learning-classifier-iris
data-visualization machine-learning python-script python3
Last synced: 8 months ago
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Algorithm(s) for identifying/predicting type of iris
- Host: GitHub
- URL: https://github.com/hfagerlund/machine-learning-classifier-iris
- Owner: hfagerlund
- License: mit
- Created: 2018-12-14T19:09:26.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-14T19:50:49.000Z (about 7 years ago)
- Last Synced: 2025-05-16T19:47:48.144Z (10 months ago)
- Topics: data-visualization, machine-learning, python-script, python3
- Language: Python
- Homepage:
- Size: 2.93 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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;
* 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
* 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-classifier-iris/blob/master/LICENSE).