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At GlobalMart, customer segmentation has always been a game of guesswork — clusters are formed, but the reasoning behind them often remains unclear. Marketing teams end up relying on pre-built models or random cluster counts, unaware of how parameters like centroids, distance metrics, and scaling influence the outcome. This masterclass demystifies clustering by helping you visualize, debug, and evaluate segmentation models end-to-end.
As a data analyst at GlobalMart, you’ll move beyond blindly applying algorithms and instead learn how to make clustering explainable and actionable. Through Python, Pandas, Matplotlib, and scikit-learn, you’ll explore clustering intuition, interpret centroids, calculate variance, and compare the quality of segmentation across different metrics. You’ll also uncover the impact of parameter tuning and understand how poorly chosen cluster settings can skew real-world marketing decisions.
By completing hands-on simulations, code fixes, and evaluations, you’ll gain the ability to create clusters that not only perform well mathematically but also make business sense. You’ll generate synthetic datasets, visualize their separation, debug broken K-Means logic, and compare hierarchical and K-Means clustering techniques — building a strong foundation for customer segmentation that drives intelligent personalization and targeted marketing campaigns.