Mall Customer Segmentation Using Machine Learning
V. Lakshman Narayana, S. Sirisha, G. Divya, N. Lakshmi Sri Pooja, Sk. Afraa Nouf
Abstract
Take our hypothetical firm as an example, and you're trying to figure out how well a particular product will perform from a marketing perspective. Customers might be segmented based on their market behaviour. Take note that the amount of data available is enormous, and that cannot process it with our human senses alone. Machine learning algorithms and processing power will be employed. Unsupervised learning has several important uses, one of which is customer segmentation. Identify client segments to focus on the possible user base by using clustering techniques (K-means, Agglomerative, and Mean Shift). As a result, they segment customers into groups based on similar factors such as gender and age as well as interests and spending habits. Gender and age patterns can also be seen using K-means clustering. After that, look at their yearly earnings and expenditure totals. When compared to the current model, which employs Mini Batch K-means, the findings from the projected model are more accurate.