End-to-end data science pipeline for supermarket re-supply strategy optimisation using clustering and time series forecasting.
DSMarket was my capstone project for the MSc Data Science at Nuclio Digital School (Barcelona). The brief: take three years of transactional retail data and design a re-supply strategy that beats the supermarket's existing rule of thumb on a held-out test period.
The pipeline runs EDA, customer and product clustering, demand forecasting per cluster (LSTM and Random Forest baselines), and ships a small dashboard that lets a category manager pick a store + cluster and see the recommended re-supply curve against the actual sell-through.
Pilot re-supply strategy with full end-to-end pipeline from raw transactional data to actionable per-store recommendations.