The purpose of the project on A/B testing involved to undertand if there is a need to adopt a new page for the online academic platform. The project found out that keeping the old page is better, and adopting a new page does not have any statistical significance. A/B tests are very commonly performed by data analysts and data scientists.For this project, I was working to understand the results of an A/B test run by an e-commerce website. The company has developed a new web page in order to try and increase the number of users who "convert," meaning the number of users who decide to pay for the company's product. My goal was to work through this notebook to help the company understand if they should implement this new page, keep the old page, or perhaps run the experiment longer to make their decision.
You need an installation of Python, plus the following libraries:
- numpy
- pandas
- matplotlib.pyplot
- statsmodels.api
- Bootstrapping sampling distributions and p-value calculations
- Z-core test
- Logistic regression
- Multiple linear regression
The conversion rate for the new page is do not provide statistical significance thus it is not better than old page