Customer Segmentation using K-Means clustering algorithm
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What is Customer Segmentation ?
Customer segmentation is a process that involves grouping customers based on shared characteristics such as age, industry, gender, shopping history, etc. Customer segmentation helps businesses learn about customers more deeply, so they can know how to market and sell their products, which customers to invest in, and how to improve their marketing techniques
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What is K-Means ?
K-means is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster . in practice, k-means works as fallows :
1- The K-means algorithm begins by initializing all the coordinates to “K” cluster centers. (The K number is an input variable and the locations can also be given as input.)
2- With every pass of the algorithm, each point is assigned to its nearest cluster center.
3- The cluster centers are then updated to be the “centers” of all the points assigned to it in that pass. This is done by re-calculating the cluster centers as the average of the points in each respective cluster.
4- The algorithm repeats until there’s a minimum change of the cluster centers from the last iteration