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.
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 February 1, 2023
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