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Large-scale demand forecasting

The notebooks listed below outline how to performed large-scale demand forecasting using DataRobot's Python package. No single model can handle extreme data diversity or forecast the complexity of human buying patterns at a detailed level. Complex demand forecasting typically requires deep statistical know-how and lengthy development projects around big data architectures. This notebook builds a model factory to automate this requirement by creating multiple projects "under the hood."

Follow the notebooks below in order to complete the demand forecasting workflow.

Download the notebook here to try it yourself

Topic Describes...
Setup and upload data Install and import the required libraries, connect to DataRobot, and curate the data for modeling.
Cluster data Break a dataset up into smaller datasets that group similar items together.
Build models Use segmented modeling to improve model performance and decrease time to deployment.
Get model insights and create feature lists Review insights for the top-performing model and create new feature lists.
Deploy a model and make predictions Test a model's prediction capabilities and deploy a model to a production environment to generate predictions.

Updated May 2, 2022
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