Python Project – Segmenting Credit Card Customers with K-Means

Project Goal: Segment credit card clients to support tailored business strategies and better meet customer needs using the K-means clustering algorithm

Project Outcome: Successfully segmented 10,127 clients into 6 distinct clusters based on demographics, spending habits, credit limits, and tenure. Recommended targeted strategies, such as credit management tools for high-utilization clients and premium rewards with low-touch services for high-value customers

Project Methods: Explored and prepared the data for modeling, then used the Elbow Method to identify 6 clusters. Examined how variables differ across clusters to understand their characteristics using various chart types. Assigned each client to a cluster and created a summary of each group’s characteristics, enabling specific and actionable recommendations