video1.0<iframe src="https://www.loom.com/embed/186ef8fa067c4650ae3a71975ac03d20" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/186ef8fa067c4650ae3a71975ac03d20-36b2b3895b692754.gif714.832In this Loom, I built a production style forecasting pipeline to predict the next 8 weeks of sales for each state. I preprocess and resample data to a weekly interval, handle missing values and duplicates, then do feature engineering with lag features, rolling mean and std, temporal features like month and quarter, and a holiday flag. I train four models per state, SARIMA, ARIMA, Profit XGBoost, and LSTM, evaluate with RMSE, MAE, and MAPE, and automatically select the best validation performer. I save trained models with joblib and serve forecasts through a FastAPI REST API with Swagger. No action is requested from viewers.