I have a full set of customer-level records—demographics, purchase history, and web-behaviour logs—waiting to be mined. The focus is clear: I want actionable customer segmentation that can guide targeted marketing, product recommendations, and personalised onboarding journeys. Your mission starts with solid data preparation. Please tidy the CSVs, resolve missing values and duplicates, and engineer any features you feel will sharpen the insights. From there, apply clustering or an equivalent technique you trust (K-Means, Hierarchical, RFM, or another you can justify) to uncover distinct groups in the data. Once the segments emerge, I’ll need two concrete deliverables: • Reproducible code: a well-commented Python notebook or R script plus a brief read-me so my in-house analyst can re-run everything without hand-holding. • Insights pack: a concise visual report or lightweight dashboard that names each segment, explains its defining traits, and highlights the commercial opportunities it opens up. Acceptance criteria are straightforward: the pipeline runs end-to-end on my sample data, at least three interpretable segments appear with supporting metrics, and the final artefacts reach me on time in a clean, Git-ready structure. If extracting meaning from customer data is your wheelhouse, I’m ready to get started.