Retail Customer Segmentation Studio With Elbow and Silhouette Analysis

Added Jun 2025 3 design docs

Retail marketers know their customers are not one audience, but without segmentation every shopper receives the same campaign. Unsupervised learning turns raw purchase and demographic data into actionable groups — provided the analyst can defend how many segments exist and how well separated they are, which is exactly where many segmentation efforts fall apart. The intern builds an interactive Streamlit application in which retail teams upload customer data as CSV, review it through pandas-powered preview tables, select features, and run K-Means clustering from scikit-learn. The app implements the Elbow method so users can plot within-cluster variance across candidate cluster counts, and complements it with silhouette score evaluation to quantify how cleanly the segments separate. NumPy handles the numerical work, interactive charts let users inspect each segment's size and character, and accompanying Jupyter notebooks document the full analysis workflow. Through the project the intern learns the complete unsupervised learning loop — feature preparation, model fitting, cluster count selection, and validation — and practices explaining the business meaning of each segment, which is precisely what analytics roles in retail demand.

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