Deep-dive into a marketing A/B test dataset with conversion rate analysis, statistical significance testing (t-test, confidence intervals, Cohen's d), and cost-per-conversion estimation.
Check the data for integrity, clean it up and prepare for deeper analysis. This showcase will delve much deeper into more complex analysis and data verification methods.
Built completely with Python in a Jupyter environment, we’ll add libraries such as scipy and statsmodels.
Our stated goal for this showcase is to use as few objects as possible in our code.
The full notebook file will contain comments regarding our process, but we want to provide additional context and a visual breakdown. Where necessary, output will be provided as captions!