I need to pinpoint where in Sri Lanka beauty-care activations will generate the greatest lift. I already have point-of-interest and population datasets in hand; your first step is to clean, standardise, and geocode them so every record lines up with the correct administrative boundary. From there we will model demand potential by blending three pillars: age distribution, income levels, and population density. Foot-traffic proxies, brand-level competition density, and enrichment from HIES, DHS, and fresh OSM POIs pulled through Overpass should round out the feature set. I work primarily in Python (pandas / GeoPandas) and visualise in QGIS plus either Power BI or Tableau, so please keep your code and outputs compatible with that stack. The end goal is a ranked shortlist of DS, GN, or ward-level areas ready for activation, complete with hotspot heatmaps and a concise activation playbook so commercial teams can move quickly. Deliverables • Interactive dashboard (Power BI or Tableau) • Slide-ready report in PowerPoint summarising findings and recommendations • Cleaned and enriched master dataset (CSV) • All spatial layers (GeoJSON) Acceptance criteria: methodology clearly documented, fully reproducible notebooks/scripts provided, and sensitivity checks included so assumptions are transparent. If that sounds like your wheelhouse, let’s explore Sri Lanka’s next beauty hotspots together.