📈 A/B Testing Analysis — Conversion Rate Experiment

This repository contains an end-to-end A/B testing analysis, covering:

• experiment design
• sample size & power analysis
• data cleaning & wrangling
• statistical testing (t-tests, χ² tests, confidence intervals)
• visualizations
• result interpretation

The goal is to demonstrate a professional-grade A/B testing workflow, similar to what mid-tier data analysts, product analysts, and growth analysts perform at product-driven companies.

More updates coming soon.

Link to project

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🚀 Run This Project with Docker Compose

This repository includes a Docker environment so anyone can run the notebook without installing Python or dependencies locally.

1️⃣ Start the container

docker compose up --build

• --build ensures Docker rebuilds the image if you updated requirements or code.
• After startup, Docker will print a Jupyter Notebook URL similar to:

http://127.0.0.1:8888/?token=xxxxx

2️⃣ Open the notebook

Copy the URL into your browser. You will see:

ab_testing.ipynb

Open it and you can start exploring the analysis.

3️⃣ Stop the container

Press:

CTRL + C

Then run:

docker compose down

This stops and removes the container cleanly.

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🧱 Project Structure

├── Data/
│   ├── pretest.csv
│   ├── test.csv
│   └── test_n.csv
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
├── ab_testing.ipynb
└── README.md

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