Customer segmentation using Machine Learning K-Means Clustering

Most of platforms build in Information Technologies are generating huge amount of data. This data is called as Big Data and it carries lots of business intelligence. This data is crossing boundaries to meet different goals and opportunities. There is opportunity to apply Machine Learning to create value for clients. Problems We have big data based platforms in Accounting and IoT domain that keep on generating customer behavior and device monitoring data. Identifying targeted customer base or deriving patterns based on different dimensions is key and really provide an edge to the platforms. Idea Imagine you got 1000’s of customers using your platform and vast amount of big data that’s keep on generating, any insight on this is really going to value add. As part of Machine Learning initiatives and innovative things that Patterns7 team keep on trying, we experimented on K-Means Clustering and value it brings to our Clients is awesome. Solution Clustering is the process of partitioning a group of data points into a small number of clusters. In this part, you will understand and learn how to implement the K-Means Clustering. K-Means Clustering K-means clustering is a method commonly used to automatically partition a data set into k groups. It is unsupervised learning algorithm. K-Means Objective The objective of k-means is to minimize the total sum of the squared distance of every point to its corresponding cluster centroid. Given a set of observations (x1, x2, …, xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k (≤ n) sets S = {S1, S2, …, Sk} so as to...