pymc-marketing

PyMC-Marketing coding conventions, preferred implementations, and style guidelines.

PyMC-Marketing coding conventions, preferred implementations, and style guidelines.

Create git commits for changes made during the session.

Create git commits for changes made during the session.

Use whenever creating or editing PyMC-Marketing documentation, including small updates to existing notebooks. Apply the repository's documentation conventions and notebook visibility policy.

Create an implementation based on a plan.

Create an implementation based on a plan.

Create a plan based on document research through an interactive, iterative process.

Create a plan based on document research through an interactive, iterative process.

Media Mix Modeling with PyMC-Marketing. Use when building MMMs, specifying adstock/saturation transformations, setting priors, fitting multidimensional (geo-level) models, computing channel contributions, ROAS, running budget optimization, calibrating with lift tests, or performing sensitivity analysis. Covers the MMM class, GeometricAdstock, LogisticSaturation, BudgetOptimizerWrapper, and ArviZ diagnostics for marketing models.

Structure a research based on the user request. Identify what must change in order to complete the task.

Structure a research based on the user request. Identify what must change in order to complete the task.

Fixing and validating Jupyter notebooks in pymc-marketing. Use when notebooks fail CI, need arviz_plots 2.x migration fixes, or when source code changes require notebook output updates.

Learn how to work in the current folder (repository)

Learn how to work in the current folder (repository)