Skip to content

nethra0906/PriceIQ

Repository files navigation

PriceIQ - Dynamic Pricing Engine

AI-powered real-time price optimization engine for e-commerce retail analytics.

Python Streamlit XGBoost


What is PriceIQ?

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.


Features

  • 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

Tech Stack

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)

Quickstart

1. Clone the repo

git clone https://github.com/yourusername/PriceIQ.git
cd PriceIQ

2. Create virtual environment

python -m venv venv
venv\Scripts\activate        # Windows
source venv/bin/activate     # Mac/Linux

3. Install dependencies

pip install -r requirements.txt

4. 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.py

5. Launch dashboard

streamlit run dashboard/app.py

Dashboard Preview

Real-time optimal price, demand curve, revenue vs profit chart, competitor analysis, and inventory alerts — all in one view.


Model Performance

Metric Value
Algorithm XGBoost Regressor
MAE ~3–5 units
R² Score ~0.85+
Training Data 2 years × 4 products

Future Improvements

  • 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

About

AI-powered dynamic pricing engine that optimizes product prices in real-time using demand forecasting, price elasticity modeling, and competitor analysis, built with XGBoost and Streamlit.

Topics

Resources

Stars

1 star

Watchers

0 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors