.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_packages_scikit-learn_auto_examples_plot_california_prediction.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_packages_scikit-learn_auto_examples_plot_california_prediction.py:


A simple regression analysis on the California housing data
===========================================================

Here we perform a simple regression analysis on the California housing
data, exploring two types of regressors.




.. code-block:: python


    from sklearn.datasets import fetch_california_housing 
    data = fetch_california_housing(as_frame=True)







Print a histogram of the quantity to predict: price



.. code-block:: python

    import matplotlib.pyplot as plt
    plt.figure(figsize=(4, 3))
    plt.hist(data.target)
    plt.xlabel('price ($1000s)')
    plt.ylabel('count')
    plt.tight_layout()




.. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_001.png
    :class: sphx-glr-single-img




Print the join histogram for each feature



.. code-block:: python


    for index, feature_name in enumerate(data.feature_names):
        plt.figure(figsize=(4, 3))
        plt.scatter(data.data[feature_name], data.target)
        plt.ylabel('Price', size=15)
        plt.xlabel(feature_name, size=15)
        plt.tight_layout()





.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_002.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_003.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_004.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_005.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_006.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_007.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_008.png
            :class: sphx-glr-multi-img

    *

      .. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_009.png
            :class: sphx-glr-multi-img




Simple prediction



.. code-block:: python


    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)

    from sklearn.linear_model import LinearRegression
    clf = LinearRegression()
    clf.fit(X_train, y_train)
    predicted = clf.predict(X_test)
    expected = y_test

    plt.figure(figsize=(4, 3))
    plt.scatter(expected, predicted)
    plt.plot([0, 50], [0, 50], '--k')
    plt.axis('tight')
    plt.xlabel('True price ($1000s)')
    plt.ylabel('Predicted price ($1000s)')
    plt.tight_layout()





.. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_010.png
    :class: sphx-glr-single-img




Prediction with gradient boosted tree



.. code-block:: python


    from sklearn.ensemble import GradientBoostingRegressor

    clf = GradientBoostingRegressor()
    clf.fit(X_train, y_train)

    predicted = clf.predict(X_test)
    expected = y_test

    plt.figure(figsize=(4, 3))
    plt.scatter(expected, predicted)
    plt.plot([0, 50], [0, 50], '--k')
    plt.axis('tight')
    plt.xlabel('True price ($1000s)')
    plt.ylabel('Predicted price ($1000s)')
    plt.tight_layout()




.. image:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_california_prediction_011.png
    :class: sphx-glr-single-img




Print the error rate



.. code-block:: python

    import numpy as np
    print("RMS: %r " % np.sqrt(np.mean((predicted - expected) ** 2)))

    plt.show()






.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    RMS: 0.5314909993118918


**Total running time of the script:** ( 0 minutes  4.479 seconds)


.. _sphx_glr_download_packages_scikit-learn_auto_examples_plot_california_prediction.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_california_prediction.py <plot_california_prediction.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_california_prediction.ipynb <plot_california_prediction.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_