Formed in 2009, the Archive Team (not to be confused with the archive.org Archive-It Team) is a rogue archivist collective dedicated to saving copies of rapidly dying or deleted websites for the sake of history and digital heritage. The group is 100% composed of volunteers and interested parties, and has expanded into a large amount of related projects for saving online and digital history.
A/B Testing to Distinguish Impact of Version of Landing Page on User
| Problem | Data | Methods | Libs | Link |
|---|---|---|---|---|
Conversion |
Retail | A\B Testing, Z test |
pandas, statsmodel |
https://github.com/erdiolmezogullari/ml-ab-testing |
In this project, A/B testing was performed on Udacity's Course dataset. It consists of 5 columns, <user_id, timestamp, group, landing_page, converted>. In A/B testing, we used 3 columns of out of them, group, landing_page, and converted.
We once simulated some experiments N times with respect to the conversion rates (control, treatment) already obtained over dataset. After got the further idea about dataset with this simulation, we supposed a null hypothesis and an alternative thesis. To claim our trueness of alternative hypothesis, we calculated z critical score by using Z test method with respect to alpha (0.05), and then we checked out beta, and power with respect to the effect size of the experiment.
Please, note that you may check out README.md under docs to get the further information about hypothesis test and A/B testing with some important photos.