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https://github.com/yrohitha/ab-testing-website


https://github.com/yrohitha/ab-testing-website

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## AB-Testing-Website

#### Description: Analyzing results of an A/B test run by an e-commerce website.

### Goal
**To share insights about if the UI team should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.**

### Install Setup

This project requires **Python 2.7** and the following Python libraries installed:

- [numpy](http://www.numpy.org/)
- [pandas](http://pandas.pydata.org)
- [matplotlib](http://matplotlib.org/)
- [random](https://docs.python.org/3/library/random.html)
- [scipy.stats](https://docs.scipy.org/doc/scipy/reference/stats.html)
- [statmodels.api](https://www.statsmodels.org/dev/api.html)

You will also need to have software installed to run and execute a [Jupyter Notebook](http://ipython.org/notebook.html)

If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.

### Statistical analysis
* [Bootstrapping sampling distributions](https://towardsdatascience.com/an-introduction-to-the-bootstrap-method-58bcb51b4d60) and p-value calculations
* Z-core test
* Logistic regression
* Multiple linear regression

### Key findings
* The conversion rate for the new page is not better than for the old page
* The country parameter of the user did not impact the conversion rate