{"id":18606060,"url":"https://github.com/hfagerlund/machine-learning-iris-analysis","last_synced_at":"2025-07-22T21:34:16.461Z","repository":{"id":164264927,"uuid":"155254868","full_name":"hfagerlund/machine-learning-iris-analysis","owner":"hfagerlund","description":"No longer maintained. 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Moved to [`machine-learning-classifier-iris`](https://github.com/hfagerlund/machine-learning-classifier-iris/).\n\nA machine learning classifier for identifying/predicting the type of iris (ie. setosa, versicolor, or virginica) based on its (petal, sepal) features.\n\n## Features\n\nData is:\n* loaded\n* described\n* visualized (somewhat)\n* split into 'train' and 'test' sets\n\nThen:\n* (2) machine learning models (ie. classifiers; supervised learning algorithms) are created;\n* the models are 'fit' to the training data;\n* (class) predictions are made for new/out-of-sample/test data;\n* the accuracy of the algorithms is evaluated and compared.\n\n## Requirements\n\n* Python v3.7.0\n* Jupyter Notebook server v5.6.0\n  * IPython v6.5.0\n* Iris flowers dataset (included with [scikit-learn](https://github.com/scikit-learn/scikit-learn))\n\n(All copyrights for the above remain with their respective owners.)\n\n## License\nCopyright (c) 2018 Heini Fagerlund. Licensed under the [MIT License](https://github.com/hfagerlund/machine-learning-iris-analysis/blob/master/LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhfagerlund%2Fmachine-learning-iris-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhfagerlund%2Fmachine-learning-iris-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhfagerlund%2Fmachine-learning-iris-analysis/lists"}