Added code for the blog post draft on Bayesian MMM with PyMC

[SalK91]

Description

This PR adds a tutorial notebook demonstrating how to implement a Bayesian Media Mix Model using PyMC.

• The notebook walks through:
  • Applying adstock transformation and Hill Function
  • Building a Bayesian regression model using PyMC
  • Interpreting media channel effects and model outputs

The notebook is intended as a companion to a future blog post, but can be reviewed independently.

Related Issue

• Closes #
• Related to internal blog content discussion with @dr. Juan Camilo Orduz

Checklist

• Checked that the pre-commit linting/style checks pass. Feel free to comment pre-commit.ci autofix to auto-fix.
• Included tests that prove the fix is effective or that the new feature works
• Added necessary documentation (docstrings and/or example notebooks) using numpydoc format.
• If you are a pro: each commit corresponds to a relevant logical change

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📚 Documentation preview 📚: https://pymc-marketing--1761.org.readthedocs.build/en/1761/

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[juanitorduz]

Hi! @SalK91 Thank you for opening this PR. Here are some comments:

• This is a PyMC pure model and the docs of this repository are intended to showcase how to use PyMC-Marketing, so you should use it instead of pure PyMC if you want this PR to be reviewed.

• Isn't this example already treated in https://www.pymc-marketing.io/en/latest/notebooks/mmm/mmm_case_study.html ? What is the difference? Maybe you can take the existing notebook of this example and extended.

Thanks

[juanitorduz]

No need to include all these files (readme, png and requirements)

[juanitorduz]

No need for an additional readme

[juanitorduz]

we should never use pip to install pymc 🙏 (but this readme should not be part of the PR)

[juanitorduz]

remove this as the examples should work with pymc-marketing and minimal dependencies (why beautifulsoup4 for example?)