Metadata-Version: 2.1
Name: ab-testing-analysis
Version: 0.0.2
Summary: A python library dedicated for A/B testing analysis
Home-page: https://github.com/mihir-workspace/ab-testing
Author: Mihir Deo
Author-email: <mihirdeo16@gmail.com>
License: UNKNOWN
Keywords: python,a/b testing,abtest,response analysis,conversion rate analysis
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy

# A/B-testing

![ab-testing-logo](https://raw.githubusercontent.com/mihir-workspace/ab-testing/main/assets/abtest_logo.png)

A/B testing is process which allows developer/data scientist to analyze and evaluate, the performance of products in an experiment. In this process two or more versions of a variable (web page, page element, products etc.) are shown to different segments of website visitors at the same time to determine which version leaves the maximum impact and drives business metrics.

In A/B testing, **A** refers to ‘control’ or the original testing variable. Whereas **B** refers to ‘treatment’ or a new version of the original testing variable. Impact of the results can be evaluated based on,
+ Conversion Rate
+ Significance test
----

#### Installation & Usage
+ Installing the library from [pypi](https://pypi.org/project/ab-testing-analysis/) - It has only dependency on *pandas & numpy*
```shell
pip install ab-testing-analysis
```
+ Usages & working sample - [Tutorial](https://github.com/mihir-workspace/ab-testing/blob/main/Docs/Tutorial-walkthrough.ipynb)
```python
from ab_testing import ABTest
ab_obj = ABTest(df,response_column='converted',group_column='group')

print(ab_obj.conversion_rate())
```
![Conversion-rate](https://raw.githubusercontent.com/mihir-workspace/ab-testing/main/assets/results-conversion-rate.png)








