Customer Segmentation

Project overview

Exploring customer groups through clustering.

A customer-segmentation workflow comparing clustering approaches for age, income and spending data. It includes input validation, reusable model artifacts and interpretable segment summaries for exploratory marketing analysis.

• Python
• Clustering
• PCA
• Evaluation

Inside this build

• Validated finite, unique customer identifiers and documented categorical values, with a 1.5×IQR income-outlier rule.
• Standardised age, income and spending features while reserving gender for post-clustering interpretation.
• Compared K-Means, agglomerative clustering and DBSCAN, with and without PCA.
• Built a configurable six-segment K-Means workflow that saves its scaler, model, assignments and metrics.
• Evaluated silhouette, Davies–Bouldin and Calinski–Harabasz scores alongside inertia, and added 15 tests with 95% coverage.
• Documented the absence of external validation, stability studies and measured campaign lift; the segments remain exploratory.

Source code & links