Building a Customer-Segmentation Engine

• ✓Build a customer-segmentation engine that turns a table of customers into named, actionable segments
• ✓Implement K-Means from scratch (assignment + update) and see why it converges
• ✓Use k-means++ initialization for stable, higher-quality clusters
• ✓Choose the right number of segments with the elbow method and silhouette score
• ✓Engineer and scale RFM (Recency, Frequency, Monetary) features from raw transactions
• ✓Profile, name, and act on segments (high-value, at-risk, new)

Run a marketing team's segmentation playbook as code — turn a customer table into named segments (high-value, at-risk, new) instead of eyeballing a spreadsheet.