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

• Python v3.7.0
• Iris flowers dataset (included with scikit-learn)

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

Copyright (c) 2018 Heini Fagerlund. Licensed under the MIT License.