Project detail
Price Optimization with PyMC
Bayesian model for estimating elasticity, demand, and expected revenue to find more profitable prices.
Highlights
- Price-demand elasticity estimation
- Optimal price calculation by expected revenue
- FastAPI backend and Flutter client
- Useful tool for commercial decisions
No public repository or demo link is published for this project yet.
Architecture
Integrated project with PyMC helper, FastAPI API, and Flutter app to explore price and revenue scenarios
Challenges
- Connecting Bayesian inference with a usable interface
- Maintaining parameter and result traceability
- Choosing practical grids and hyperparameters for business
Outcome
A tool for testing prices in a more informed and less intuitive way
Overview
Problem
Setting prices without measuring elasticity risks losing revenue or demand due to uninformed decisions.
Approach
I built a Bayesian workflow that estimates the price-sales relationship and returns the most profitable point.
Outcome
The system enables price experimentation with expected revenue impact visualization before implementation.