A hobby botanist is interested in distinguishing the species of some iris flowers that she has found. She has collected some measurements associated with each iris: the length and width of the petals and the length and width of the sepals, all measured in centimeters.
She also has the measurements of some irises that have been previously identified by an expert botanist as belonging to the species setosa, versicolor, or virginica. For these measurements, she can be certain of which species each iris belongs to. Let’s assume that these are the only species our hobby botanist will encounter in the wild.
Our goal is to build a machine learning model that can learn from the measurements of these irises whose species is known, so that we can predict the species for a new iris.
From the figure given below it is easy to observe that every feature distinctly differentiates the species of the iris. So every feature was considered while making the prediction. Then KNeighborsClassifier was used to train the dataset. The classifier gave a perfect accuracy of 100%, mainly owing to the small size of the dataset.
