Build Dataverse revenue forecasts with Python linear regression
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Use the Dataverse SDK for Python, Pandas and Scikit-Learn to retrieve business data, predict future revenue and save results back to Dataverse.
Original article from September 25, 2026: Linear Regression with Microsoft Dataverse: Predictive Analytics Using Python, by Pavan Mani Deep Y, on Ecellors Blog. Read the original
This is our own summary, not a republication or full translation.
TL;DR
The Dataverse SDK for Python retrieves monthly revenue records into a Pandas DataFrame, Scikit-Learn trains a linear regression model, and predictions for future months are written back to Dataverse for Power Apps, Power BI or Copilot experiences.
Why it matters
- Makers: Use the documented Dataverse table pattern to expose prediction results to Power Apps or other business experiences.
- BI/Data teams: Retrieve Dataverse records with the Python SDK, prepare them in Pandas and train the example regression model with Scikit-Learn.
- Leadership/Business: Consider historical revenue, ticket or demand data as inputs for forecasting scenarios described in the guide.
Frequently asked questions
How can Python retrieve Dataverse data for a linear regression model?
The Dataverse SDK for Python queries selected table columns and loads the returned records into a Pandas DataFrame for analysis.
How does the example predict future revenue from Dataverse records?
The example trains Scikit-Learn LinearRegression on month and revenue columns, then predicts revenue for months 6, 7 and 8.
How can Dataverse store Python forecast results for Power Apps?
The Python client creates records in a revenue prediction table, storing each future month and its predicted revenue for downstream experiences.