Unlock the power of customer segmentation by learning how to analyze, model, and visualize shopping behavior using Python and K-Means clustering. In this hands-on course, you'll work through a practical workflow that transforms customer data into meaningful business insights using unsupervised machine learning techniques.

You'll begin by preparing customer datasets, configuring your analysis environment, and creating visualizations such as pie charts, histograms, violin plots, and pair plots to explore customer characteristics. Next, you'll examine relationships between variables through correlation analysis, prepare data for clustering, and build a K-Means model. Finally, you'll visualize customer clusters, evaluate segmentation results, and interpret shopping behavior to support informed marketing and business decisions. Designed for learners interested in data analysis, machine learning, and customer analytics, this course emphasizes a structured, end-to-end approach—from data exploration and preprocessing to clustering and insight generation. By combining visualization, modeling, and cluster interpretation within a single learning experience, you'll gain practical skills for analyzing customer behavior and identifying meaningful customer segments using real-world data. Enroll to develop a systematic approach to customer segmentation and learn how data-driven analysis can support more informed business strategies.