Customer Segmentation with K-Means: Model & Visualize

This practical course equips learners with the analytical skills to explore, model, and visualize customer shopping behavior using Python and K-Means clustering. Through structured modules, learners will prepare real-world customer data, construct meaningful visualizations, analyze variable relationships, and evaluate clustering outcomes to derive actionable business insights. Starting with data preprocessing and environment setup, learners will organize datasets and construct various statistical charts, including pie charts, histograms, and violin plots, to interpret customer attributes. Building on this foundation, the course guides learners through correlation analysis, scaling, and model development using the K-Means algorithm. Finally, learners will visualize customer clusters and assess shopping behavior to support strategic segmentation and personalized marketing decisions. By the end of this course, learners will be able to apply unsupervised machine learning techniques to segment customers and formulate data-driven business insights from complex shopping datasets

• Preprocess and organize customer data for clustering analysis in Python
• Build and apply K-Means models to segment customers effectively
• Visualize clusters and interpret insights for strategic business decisions
• Analyze variable relationships and scale data before modeling

• No deep prior experience is required, but basic computer and internet skills are helpful
• Ability to read course instructions in English and complete short practice activities