Algorithmic Pricing Engine & A/B Testing Simulator

An enterprise-grade analytical pipeline that evaluates how structural pricing changes impact conversion mechanics and gross margins, utilizing frequentist hypothesis testing to validate statistical significance.

Tech Stack & Architecture

• Core Language: Python (scipy.stats, statsmodels, pandas, numpy)
• Interface & BI Visuals: Streamlit, Plotly Canvas
• Infrastructure Layer: Docker Containers
• Automation Pipeline: GitHub Actions (CI/CD)

Core Methodologies Featured

• Price Elasticity of Demand (PED): Modeling nonlinear demand curves based on price fluctuations.
• Power Analysis: Quantifying sample size limitations required to reach statistical bounds before running experiments.
• Frequentist Inference: Executing two-proportion Z-tests to explicitly reject or fail to reject the null hypothesis.
• Unit Economics: Mapping conversion lifts directly to Revenue Per Visitor (RPV) and projected net profit margins.

Quick-Start Docker Deployment

Ensure your local Docker Desktop engine is active, then execute:

# Build the production application layer
docker build -t pricing-ab-simulator:v1.0 .

# Run the container isolated on port 8501
docker run -d -p 8501:8501 --name elastic-pricing-engine pricing-ab-simulator:v1.0

Once deployed, navigate to http://localhost:8501 to access the interactive analytics control room.