docs: replace houseprice dataset with sklearn's fetch_california_housing (#692)

[snehagahlot3]

…ing (#692)

Replaced the houseprice.csv dataset with sklearn's
fetch_california_housing across:

• docs/user_guide/discretisation/GeometricWidthDiscretiser.rst
• docs/user_guide/wrappers/Wrapper.rst

Changes:

• Replaced pd.read_csv('houseprice.csv') with fetch_california_housing
• Updated target column: SalePrice → MedHouseVal
• Updated feature columns to match new dataset

[snehagahlot3]

Hi @solegalli! I'm Sneha, a GSoC 2026 applicant. I submitted this PR as part of my interest in contributing to Feature-engine. I also commented on issue #691 about implementing partial_fit as a potential GSoC project. Would love your guidance on whether this would make a suitable GSoC 2026 proposal for Feature-engine under GC-OS!

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 98.27%. Comparing base (f72a2b7) to head (f1ed2f4).

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #918   +/-   ##
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  Coverage   98.27%   98.27%           
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  Files         116      116           
  Lines        4978     4978           
  Branches      795      795           
=======================================
  Hits         4892     4892           
  Misses         55       55           
  Partials       31       31           

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[solegalli]

Hi @snehagahlot3

Thanks a lot for submitting the PR.

I'd prefer if we continue using the house prices dataset, but load it using sklearn's datasets api as we do in this file for example: https://github.com/feature-engine/feature_engine/blob/main/docs/user_guide/imputation/CategoricalImputer.rst

Also, for the PR: could you keep it focused on one module?

So for example in 1 PR you modify the user guide for the discretisation module, that is, all the discretisers in that module. In a separate PR you update the wrappers, and so on. Note that I already updated the imputation module.

This speeds up review an helps other contribute on the same issue.

Thanks a lot!