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ABexp
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.. include:: badges.rst

A/B testing is a methodology based on user experience which consists in comparing two or more variants of a single
variable and determining which one performs better according to predefined criteria. It includes wide applications in
the field of statistics.

Many industries have been using A/B test to optimize business processes and user experience. There are existing tools
and/or public libraries available to address this problem (mainly implemented in ``R``, fewer in ``Python``). However,
they typically hide the statistical techniques running under the hood and they are mostly focused on a very specific
aspect of the end-to-end experiment flow (e.g. a tool for post A/B test analysis which uses frequentist statistical
techniques).

A/B testing is a sensitive and critical aspect within Playtika organization. This pushed us to tackled this problem
drawing attention to today's state-of-the art techniques.

**ABexp**  is a ``Python`` library which aims to support users along the entire end-to-end A/B test
experiment flow (see picture below). It contains A/B testing modules which use both frequentist and bayesian
statistical approaches including bayesian generalized linear model (GLM).

.. image:: img/experiment_flow.png
  :width: 1000
  :alt: A/B experiment flow end-to-end


ABexp also provides detailed documentation and tutorials in order to help and guide users in running A/B test
experiments.

.. note::
    ABexp is a generic library that can be directly used by analysts and data scientists as standalone product when
    imported in  ``Jupyter Notebook`` or ``Python`` projects. It can also be used in the background of more complex
    products that expose its functionalities through a user interface.