🧠 Smart Dynamic Pricing System

A fully modular and production-grade AI-powered pricing engine built using Deep Reinforcement Learning to dynamically adjust product prices based on customer behavior, time of day, competition, and market demand.

---

🚀 Project Highlights

• 🎯 Dueling DQN Agent for stable and efficient learning
• 🛒 Simulated E-commerce Environment with customer segments, competitor dynamics & time-based pricing
• 📊 Human Baseline Comparisons (Fixed, Adaptive, Time-based, Combined)
• 🌐 React Dashboard to visualize product trends, customer segmentation & agent performance
• 🔬 Reward System to track improvement over human logic

---

🧠 Architecture Overview

Frontend (React Dashboard)
↓
Flask API Server
↓
+----------------------+
\|   Dueling DQN Agent  |
\|   Reward System      |
\|   Market Environment |
+----------------------+
↓
Customer Segments, Products, Time-of-Day, Competitor Pricing

---

📂 Project Structure

smart-dynamic-pricing/
│
├── frontend/               # React + Tailwind dashboard
│   ├── src/
│   ├── public/
│   └── …
│
├── backend/                # Flask + DQN model + API
│   ├── enhanced\_api.py
│   ├── enhanced\_agent.py
│   ├── enhanced\_env.py
│   ├── human\_baseline.py
│   └── enhanced\_reward\_system.py
│

└── README.md

---

🛠️ Setup & Installation

Frontend

cd frontend
npm install
npm run dev

Runs at http://localhost:3000/

Backend (Flask API)

cd backend
pip install -r requirements.txt
python enhanced_api.py

Runs at http://localhost:5000/

Requirements: Python 3.8+, Node.js 16+

---

📈 Live Dashboard Previews

---

🧪 How It Works

1. The agent uses a Dueling Double DQN to learn pricing strategies.

2. The environment simulates:

   • Time-of-day influence
   • Customer segment preferences
   • Competitor price adjustments

3. The agent is rewarded based on:

   • 💰 Profit earned
   • 📈 Improvement over human baseline strategies

4. All insights are visualized in a user-friendly React frontend.

---

📜 License

This project is licensed under the MIT License.

---

🙌 Credits

• Developers:

  • Lakshit Mundra
  • Parth Tripathi
  • Arnav Deshmukh

• Tech Stack:

  • 🧠 TensorFlow (Deep RL)
  • ⚙️ Flask (API Backend)
  • 🌐 React + Tailwind CSS (Frontend Dashboard)

---

🌟 If you found this helpful, please give it a star!