Segmenting Mall Customers Data to Improve Business into Higher Target using K-Means Clustering

Every day we deal with a vast amount of data. To analyze those data is such a big task but also important. This paper demonstrates the segmentation of customers of e-commerce website based on a machine learning algorithm namely K-Means Clustering. We have collected the data of around 300 customers from an e-commerce website and their total yearly pay on an e-commerce website and then we will analyze it and found the number of optimum clusters. Now a day’s every company is targeting their customers based on the available data and trying to find out the behaviors of customers like which kind of product most attracting them, what they like. Based on that customer’s behavior companies are launching their new products. We will use the K-Means Clustering Algorithm a machine learning-based method to find the optimum number of clusters and analyze the underlying client segments supported by the information provided. In today’s world, it is hard to get the customers behavior and categorizing the customers into different clusters and categories’ customers supported their human ecology and shopping for behavior. This could be a crucial side of client partition that permits the business to higher target their customers increase in selling’s and modify their products according to their targeted customers and launching new products.