Sales Forecasting in Python: Beat the Baselines on a Hidden Future

<p>Malwa Crunch Foods makes namkeen in Indore and plans its fryers and raw material weeks ahead. Diwali is coming, and the production head wants a weekly forecast per pack for the next eight weeks, with an honest idea of how wrong it will be. Ishita Verma, the new demand planner, has a raw ERP export, a stockout log, the promotion plan and a festival calendar.</p><p>Across five lessons you build her forecast in Python on your own copy of the data: turning daily rows into a clean weekly series (re-sent days, a changed pack code, a partial last week, Monday week labels); correcting sales for stockouts so the model learns demand, not stock; an honest time-based backtest against naive, seasonal naive and moving-average baselines scored with MAPE; and one log-linear regression per pack on trend, season, planned promotions and festivals, fitted without leaking the holdout. In every lesson you build the step yourself first, then hand the same job to your AI (ChatGPT, Claude, Copilot or Gemini) and learn exactly where it goes wrong: Sunday week labels, averages instead of totals, baselines that peek at the future, models fitted on the weeks they are scored on, coefficients read as lifts.</p><p>In the capstone your AI builds the whole pipeline from one brief, you prove every number, and Bartman scores your forecast against the eight weeks that really happened next. Those weeks never leave Bartman's server, so the score is a true test of the future, not of the past.</p>

Tools: Python, pandas, NumPy, matplotlib, Time Series Forecasting, Demand Planning, Backtesting, Regression, AI Assistants for Python