Learn to build and visualize K-means models to solve clustering problems

The K-Means clustering beams at partitioning the ‘n’ number of observations into a mentioned number of ‘k’ clusters (produces sphere-like clusters). The K-Means is an unsupervised learning algorithm and one of the simplest algorithm used for clustering tasks. The K-Means divides the data into non-overlapping subsets without any cluster-internal structure. The values which are within a cluster are very similar to each other but, the values across different clusters vary enormously. K-Means clustering works really well with medium and large-sized data.

Despite the algorithm’s simplicity, K-Means is still powerful for clustering cases in data science. In this article, we are going to tackle a clustering problem which is customer segmentation (dividing customers into groups based on similar characteristics) using the K-means algorithm. Now let’s see a little bit about the case we are going to solve.

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This article compiles the 38 top Python libraries for data science, data visualization & machine learning,

Practice your skills in Data Science with Python, by learning and then trying all these hands-on, interactive projects, that I have posted for you.