libuplift.datasets.MarketingAB
==============================

.. py:module:: libuplift.datasets.MarketingAB

.. autoapi-nested-parse::

   An A/B testing dataset from Kaggle.

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Functions
---------

.. autoapisummary::

   libuplift.datasets.MarketingAB.fetch_marketing_AB


Module Contents
---------------

.. py:function:: fetch_marketing_AB(data_home=None, download_if_missing=True, random_state=None, shuffle=False, categ_as_strings=False, return_X_y=False, as_frame=False)

   
   Load the marketing_AB dataset from Kaggle.
   Download it if necessary.

   See https://www.kaggle.com/datasets/faviovaz/marketing-ab-testing
   for details.

   The treatment was showing the user an advertisement ('ad'), the
   control showing a Public Service Announcement ('psa').

   The dataset exhibits very high class and treatment imbalance.

   Changes made to the original dataset:
    * removed record number column
    * changed spaces to _ in column names
    * changed target from bool to {0,1}

   :Parameters:

       **data_home** : string, optional
           Specify another download and cache folder for the datasets. By default
           all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

       **download_if_missing** : boolean, default=True
           If False, raise a IOError if the data is not locally available
           instead of trying to download the data from the source site.

       **random_state** : int, RandomState instance or None (default)
           Determines random number generation for dataset shuffling. Pass an int
           for reproducible output across multiple function calls.

       **shuffle** : bool, default=False
           Whether to shuffle dataset.

       **categ_as_strings** : bool, default=False
           Whether to return categorical variables as strings.

       **return_X_y** : boolean, default=False.
           If True, returns ``(data.data, data.target)`` instead of a Bunch
           object.

       **as_frame** : boolean, default=False
           If True features are returned as pandas DataFrame.  If False
           features are returned as object or float array.  Float array
           is returned if all features are floats.



   :Returns:

       **dataset** : dict-like object with the following attributes:
           ..

       **dataset.data** : numpy array
           Each row corresponds to the features in the dataset.

       **dataset.target_purchase** : numpy array
           Indicator whether a purchase was made.

       **dataset.DESCR** : string
           Description of the dataset.

       **(data, target_purchase)** : tuple if
           ``return_X_y`` is True











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