libuplift.datasets.MarketingAB#

An A/B testing dataset from Kaggle.

Functions#

fetch_marketing_AB([data_home, download_if_missing, ...])

Load the marketing_AB dataset from Kaggle.

Module Contents#

libuplift.datasets.MarketingAB.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)[source]#

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_homestring, 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_missingboolean, 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_stateint, RandomState instance or None (default)

Determines random number generation for dataset shuffling. Pass an int for reproducible output across multiple function calls.

shufflebool, default=False

Whether to shuffle dataset.

categ_as_stringsbool, default=False

Whether to return categorical variables as strings.

return_X_yboolean, default=False.

If True, returns (data.data, data.target) instead of a Bunch object.

as_frameboolean, 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:
datasetdict-like object with the following attributes:
dataset.datanumpy array

Each row corresponds to the features in the dataset.

dataset.target_purchasenumpy array

Indicator whether a purchase was made.

dataset.DESCRstring

Description of the dataset.

(data, target_purchase)tuple if

return_X_y is True