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

Install Dependencies
```
pip install -r requirements.txt
```