libuplift.datasets.Starbucks#

The Starbucks dataset.

Functions#

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

Load the Starbucks dataset.

Module Contents#

libuplift.datasets.Starbucks.fetch_Starbucks(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 Starbucks dataset.

Download it if necessary. There are many versions of this dataset, here the one from https://raw.githubusercontent.com/01KAT1/Marketing-Promotion-Campaign-Uplift-Modelling-Starbucks-Dataset/main/training.csv is used since it is easy to use and has been used in many uplift modeling papers. An original version consisting of several tables can be found at Shuniy/starbucks

See also an online post about analyzing the data: https://medium.com/@nesreensada/how-to-build-a-profitable-promotion-strategy-easily-with-uplift-modeling-26b2addc3e46

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