I have a clean set of operational data and I want to turn it into a reliable sales-forecasting model. The raw files are already exported from our ERP system (CSV and SQL dumps), covering inventory movements, order cycle times, and fulfilment logs for the past three years. What I need is a concise analysis that translates these operational patterns into forward-looking sales projections I can plug into our budgeting process. Here’s what success looks like to me: • An exploratory overview that highlights seasonality, lead-time impacts, and any outliers worth cleaning before modelling. • A forecasting model—preferably in Python (pandas, scikit-learn or Prophet are all fine)—with clear, well-commented code so my in-house team can retrain it as new data comes in. • A short write-up (slides or PDF) explaining assumptions, key drivers, forecast accuracy metrics, and how the model can be tuned as operations evolve. I will supply the data via a secure cloud folder as soon as we start. Let me know which forecasting approach you favour and how quickly you can turn an initial proof of concept.