Dynamic Pricing Optimization

Overview Link to heading

This project builds an AI-driven dynamic pricing optimization model for retail and e-commerce. The solution leverages machine learning algorithms to adjust pricing strategies in real-time based on historical trends, demand elasticity, and external market conditions.

Dataset Link to heading

• Source: Dynamic Pricing Dataset (Kaggle)
• Description: A dataset containing historical sales, pricing, and demand data to train models for price elasticity analysis and revenue maximization.

Repository & Deployment Link to heading

• 🔗 GitHub Repository: View on GitHub
• 🚀 Live Demo: Try on Hugging Face

Features Link to heading

• Automated price optimization using machine learning.
• Real-time pricing strategy recommendations.
• Deployment-ready interactive dashboard using Gradio.

Tools and Libraries Link to heading

• Python
• Pandas, NumPy
• Scikit-learn
• XGBoost
• Hugging Face Spaces
• Gradio (for interactive model deployment)

How to Run Link to heading

1. Clone the repository:
   git clone https://github.com/Sharma-Pranav/Portfolio.git
2. Navigate to the project directory:
   cd RetailDemandForecaster
3. Install dependencies:
   pip install -r requirements.txt
4. Run the Streamlit app:
   streamlit run app.py

Results Link to heading

• Enhanced forecasting accuracy through feature engineering.
• Visualization of key trends and seasonality.