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K-means is an unsupervised machine learning algorithm used to find groups of observations (clusters) that share similar characteristics. A cluster is defined as a group of observations that are more similar to each other than they are to the observations in other groups. Cluster analysis is widely used in the biological and behavioral sciences, marketing, and medical research. For example, a psychological researcher might cluster data on the symptoms and demographics of depressed patients, seeking to uncover subtypes of depression. The hope would be that finding such subtypes might lead to more targeted and effective treatments and a better understanding of the disorder. Marketing researchers use cluster analysis as a customer-segmentation strategy. Customers are arranged into clusters based on the similarity of their demographics and buying behaviors. Marketing campaigns are then tailored to appeal to one or more of these subgroups. The two most popular clustering approaches are hierarchical agglomerative clustering and partitioning clustering. In this topic, we are discussing about K means clustering which comes under partitioning clustering.