Customer Segmentation Using K-Means Clustering in Unsupervised Machine Learning

The new era’s perspective is one of creativity, in which everybody is competing to be better than the others. Today’s businesses are built on the potential of creativity to enslave consumers with goods, but with too many options, customers are left unsure of what to buy and what not to buy, and companies are focused on which segments of customers to target to sell their products. Various algorithms are employed to uncover hidden trends in data so that future judgments can be made with confidence. The uncertain definition of which segment to target can be resolved by employing segmentation. A python program was developed, and it was trained using a dataset with two features: 250 training samples taken from a local retail shop and a standard scalar. Both of these characteristics are the average of the amount of shopping done by consumers and the average of the customer’s annual visits to the store. We will be able to classify the target customers using clustering based on demographic information (such as gender, age, income, and so on), geographical information, psychographics, and behavioral data.