AI-powered real-time price optimization engine for e-commerce retail analytics.
PriceIQ is a dynamic pricing engine that simulates what companies like Amazon, Uber, and Flipkart do every day - automatically adjusting product prices based on demand, inventory, competitor pricing, and seasonal trends to maximize revenue and profit.
- Demand Forecasting - XGBoost model predicts units sold at any price point
- Price Elasticity Analysis - Measures how sensitive demand is to price changes
- Optimization Engine - Grid search across price range to find max-profit price
- Competitor Intelligence - Tracks price gap and triggers undercut alerts
- Inventory Risk Alerts - Warns when stock is critically low
- Interactive Dashboard - Built with Streamlit + Plotly for real-time insights
| Layer | Tools |
|---|---|
| Language | Python 3.10+ |
| ML Model | XGBoost, Scikit-learn |
| Forecasting | Prophet |
| Optimization | Scipy, NumPy Grid Search |
| Dashboard | Streamlit, Plotly |
| Data | Pandas, Faker (synthetic) |
1. Clone the repo
git clone https://github.com/yourusername/PriceIQ.git
cd PriceIQ2. Create virtual environment
python -m venv venv
venv\Scripts\activate # Windows
source venv/bin/activate # Mac/Linux3. Install dependencies
pip install -r requirements.txt4. Run the pipeline
python src/data_generator.py
python src/data_cleaning.py
python src/price_elasticity.py
python src/demand_forecasting.py
python src/optimization.py5. Launch dashboard
streamlit run dashboard/app.pyReal-time optimal price, demand curve, revenue vs profit chart, competitor analysis, and inventory alerts — all in one view.
| Metric | Value |
|---|---|
| Algorithm | XGBoost Regressor |
| MAE | ~3–5 units |
| R² Score | ~0.85+ |
| Training Data | 2 years × 4 products |
- Reinforcement Learning pricing agent (Q-Learning / DQN)
- LLM-powered insight layer explaining price changes
- Real-time competitor price scraping
- REST API with FastAPI for integration
- Docker containerization