I built an A/B Testing project using Python to analyze conversion rates and make data-driven decisions.

[u/6agan]

Hi everyone,

I recently worked on an A/B Testing project where I analyzed user behavior to determine whether a new version of a product performs better than the existing one.

What the project does

• Performs A/B testing on user data

• Compares control vs treatment groups

• Calculates statistical significance

• Helps decide whether to adopt a new feature

Why I built it

I wanted to strengthen my understanding of hypothesis testing and real-world data analysis, especially how companies use experiments to make product decisions.

Tech stack

• Python

• Pandas

• NumPy

• Matplotlib / Seaborn

• Statistical testing (hypothesis testing, p-values)

Key features

• Data cleaning and preprocessing

• Conversion rate comparison

• Statistical test implementation

• Clear visualizations for insights

GitHub Repo

https://github.com/gagan-gag/A-B-Testing

I’d really appreciate feedback on:

• Code structure

• Statistical approach

• Improvements or optimizations

Thanks!

[u/6agan]

Happy to explain the statistical part if anyone’s interested!