This project applies K-Means clustering to retail customer segmentation and incorporates market basket analysis to identify product purchasing patterns. The system clusters customers based on their buying behavior and identifies the most popular product combinations. These insights help retailers optimize inventory, improve product placements, and design targeted promotions.
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