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Setting the scene
A simple example, using both K-nearest neighbour classification and K-means clustering on the same Iris dataset and the difference between the two.
A step by step introductory example.
Given numerical data of dimensions of petals and sepals of three different species of Iris flowers, how can we make a machine learning model able to predict which of the three species new data points come from?
To solve this problem we can use either classification via an algorithm called K-nearest Neighbour (KNN) or we can use clustering via an algorithm called K-means, as we will see in this video.
More of the details of the KNN algorithm, including Euclidian distance, are in the article following this video.
