Customer Segmentation Using K-means Clustering

Customers have always been and always will be the center of the market and its most important element. As time goes on, it becomes increasingly clear how different types of customers affect market and product strategies as a whole, thanks in large part to the wealth of data that is now readily available. Most people prefer online purchasing, especially after the pandemic, which has helped boost data about the customers, their preferences, and their traits and has consequently helped firms understand their demands better. One of the methods to better provide for consumers is by implementing customer segmentation. Customer segmentation is the approach via which we may construct groups of clients depending on different elements from their already obtained data, this might be based on gender, area, age, etc. Practicing client segmentation helps the organization understand its prospective audience better which in return helps it produce better marketing schemes targeting certain zones which may assist in enhancing its product growth. Here we have developed customer segmentation bearing in mind the above-mentioned trinketed attempt to turn a system that can help predict the items that the client might be eager to buy using K-means clustering. To develop the system, we have utilized the K-means algorithm using Python language with the support of Machine Learning and Data Science methodologies. The data set used here provides real-time data about products brought along with other important information.