| # Customer Agent RL | |
| ## Overview | |
| This project simulates an advanced customer behavior analysis using an RL agent (Deep Q-Network) based on a comprehensive synthetic dataset covering multiple sectors (Retail, E-commerce, Banking, Telecom, Travel). The dataset (30,000+ records) is generated using research insights and inserted into a local MongoDB database. | |
| ## File Structure | |
| [AI_CUSTOMER_BEHAVIOR/ | |
| │── data/ | |
| │ └── customers.csv # Generated synthetic customer records (30,000+) | |
| │── models/ | |
| │ └── dqn_model.pth # Saved RL model weights after training | |
| │── src/ | |
| │ ├── generate_data.py # Script to generate a comprehensive multi-sector dataset | |
| │ ├── insert_data.py # Script to load the CSV and insert data into MongoDB | |
| │ ├── rl_agent.py # Advanced Deep Q-Network (DQN) agent implementation (O³ model style) | |
| │ └── dashboard.py # Streamlit dashboard to visualize customer behavior insights | |
| │── notebooks/ | |
| │ └── exploration.ipynb # Notebook for exploratory analysis and experiments | |
| │── requirements.txt # Dependencies for the project | |
| │── config.yaml # Configuration file for settings (MongoDB URI, hyperparameters, etc.) | |
| └── README.md # Documentation and instructions | |
| ## Setup & Usage | |
| 1. **Install Dependencies** | |
| ```bash | |
| pip install -r requirements.txt | |