Unsupervised Shopper Segmentation Tool With Exportable Cluster Reports

Added Jun 2025 3 design docs

Growth teams at retail companies need more than a clustering chart — they need segment assignments they can push into a campaign tool, and evidence that the segments are statistically sound. This project builds a segmentation tool designed around that end-to-end need, treating the export as seriously as the algorithm. The intern implements a Streamlit application that runs K-Means clustering from scikit-learn on uploaded retail customer data, then guides users through choosing a cluster count with Elbow curves and validating separation quality with silhouette scores. An interactive cluster visualizer maps customers across chosen feature pairs with color-coded segments, and pandas powers an export flow that lets users download the labeled dataset and per-cluster summary statistics for use in downstream marketing systems. NumPy underpins the numeric computation, keeping the interface responsive on realistic dataset sizes. The intern comes away understanding unsupervised learning as a product feature rather than a one-off analysis: how to validate clusters honestly, how to present them so business users trust them, and how to deliver results in a form other tools can consume — a distinctly entrepreneurial framing of a core data science skill.

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