Customer segmentation using clustering

Customer segmentation is a pivotal strategy for businesses to tailor their marketing efforts and optimize customer satisfaction. This project focuses on utilizing K-means clustering, a popular unsupervised learning algorithm, to partition customers into distinct segments based on their demographic attributes, annual income, and spending behaviour. Through comprehensive data exploration and visualization techniques, we analyse the distribution of customer characteristics and identify meaningful clusters. By applying K-means clustering, we aim to uncover hidden patterns and preferences among customers, enabling businesses to develop targeted marketing strategies and enhance customer engagement. The effectiveness of the segmentation process is evaluated through metrics such as silhouette score and within-cluster sum of squares (WCSS). Insights gained from this analysis can empower businesses with valuable insights into customer behavior, facilitating informed decision-making and personalized customer experiences. This project contributes to the field of customer relationship management by providing a data-driven approach to segmentation and highlighting the significance of clustering algorithms in understanding customer dynamics.