📊 Customer Segmentation using K-Means Clustering — AI Internship @ SmartED Innovations One of the most practical ML problems in retail: who are your customers, really? For my minor project at SmartED Innovations, I built a complete customer segmentation pipeline using K-Means clustering — identifying 5 distinct buyer personas from a 200-customer retail dataset. What the model found: → 🟢 Cluster 3 — High income + high spenders → VIP targets for loyalty programmes → 🔵 Cluster 1 — High income + moderate spend → Upsell candidates → 🔴 Cluster 2 — Low income + high spend → Impulsive buyers, respond to flash sales → 🟠 Cluster 0 — High income + low spend → Conservative savers, need trust-building → 🟣 Cluster 4 — Low income + low spend → Budget shoppers, discount-sensitive Technical pipeline: → Feature selection: Annual Income + Spending Score → StandardScaler for feature normalisation → Elbow Method to determine optimal k=5 → K-Means++ initialisation for stable convergence → Seaborn scatter plot with centroid markers for visual validation Tech: Python · pandas · scikit-learn · matplotlib · seaborn 🔗 GitHub: github.com/iamadhitya1/customer-segmentation-retail #MachineLearning #Python #KMeans #CustomerSegmentation #DataScience #UnsupervisedLearning #RetailAnalytics #AIInternship #SmartEDInnovations #IITRAM