As the most popular clustering algorithm K-Means is utilized in many unsupervised Machine Learning projects the extract meaning from seemingly chaotic unlabeled data.
K-Means opens new doors for Machine Learning applications thanks to its ability to cluster unlabeled data.
This step by step tutorial is intended as a basic walkthrough of a typical K-Means implementation.
It's crucial to understand the fundamental hyperparameters of K-Means machine learning models and to be able to tweak them when necessary.
Like all Machine Learning algorithms K-Means is not perfect. It's important to know where it shines and where it doesn't so that expectations are well aligned and right algorithms are chosen for the right projects.
Read about the history of K-Means Algoritm.