Data Analysis in Shopping Mall data using K - Means Clustering

Now a days in modern era, a shopping mall takes up large part of people's lives. For the mall operator, store design is of paramount importance as it increases customer satisfaction and business profit. Finding a sensible layout for a shopping center, however, remains a challenging and long-term challenge. Based on this scrutiny, a machine learning approach is proposed to evaluate preferred arrangement of a shopping mall. With this scrutiny, information about transactions is collected from banks as input. Many shopping malls have basic analysis system for analyzing data of customer, data of each and every transaction including demo-graphic data, gender, age and income. They include additional features to shop-ping malls to maintain loyalty of customer. In this paper, we have a shopping center analyze customer buying behavior. We rank customers based on how often they shop, how much they buy, and what the value of the item purchased is. Applied the K-Means algorithm to group based on the customer's annual income and spending money, the customer's age, and the money spent at the mall, spending money, customer age and spending money in shopping mall.