DOC california housing example (#308)

To quote:

The goal of this exercise is to go through a semi-realistic data science
and machine learning task and develop a practical solution for it. We
will learn about the following topics:

- Perform *exploratory data analysis*
- Do some non-trivial *feature engineering*
- Explain how the feature engineering informs the *choice of machine
  learning model* and vice versa
- Show how to make use of a couple of *advanced scikit-learn* features
  and explain why we use them - Create a *model card* that provides
  useful information about the model
- Share the model by uploading it to the *Hugging Face Hub*

docs/examples.rst

@@ -4,19 +4,24 @@ Examples of interactions with the Hugging Face Hub
 ==================================================
 
 - Creating the Model Card:
-    :ref:`sphx_glr_auto_examples_plot_model_card.py` is an example of using 
+    :ref:`sphx_glr_auto_examples_plot_model_card.py` is an example of using
     skops to create a model card that can be used on the Hugging Face Hub.
 - Putting the Model Card on the Hub:
-    :ref:`sphx_glr_auto_examples_plot_hf_hub.py` is an example of using skops 
+    :ref:`sphx_glr_auto_examples_plot_hf_hub.py` is an example of using skops
     to put a model card on the Hugging Face Hub.
 - Tabular Regression:
-    :ref:`sphx_glr_auto_examples_plot_tabular_regression.py` is an example of using skops to serialize a tabular 
+    :ref:`sphx_glr_auto_examples_plot_tabular_regression.py` is an example of using skops to serialize a tabular
     regression model and create a model card and a Hugging Face Hub repository.
 - Text Classification:
-    :ref:`sphx_glr_auto_examples_plot_text_classification.py` is an example of using skops to serialize a text 
+    :ref:`sphx_glr_auto_examples_plot_text_classification.py` is an example of using skops to serialize a text
     classification model and create a model card and a Hugging Face Hub repository.
 - Using Intel(R) Extension for scikit-learn:
     :ref:`sphx_glr_auto_examples_plot_intelex.py` is an example of using
     Intel(R) Extension for scikit-learn to speed up inference of classical
     machine learning algorithms and how performance-optimized models work with
     Hugging Face Hub.
+- Long semi-realistic guide using the California Housing dataset:
+    :ref:`sphx_glr_auto_examples_plot_california_housing.py` is an exercise that
+    goes through a semi-realistic data science and machine learning task and
+    develops a practical solution for it. It uses skops for model saving, model
+    card generation, and upload to the Hugging Face Hub.