This repository contains an implementation of the Google PageRank algorithm using a Markov Decision Process (MDP). The PageRank algorithm is used to rank web pages in search engine results by calculating the probability of a user randomly clicking on links reaching a particular page. By modeling this as an MDP, we can leverage the theory and tools from reinforcement learning and stochastic processes to enhance and analyze the PageRank.

• Python 3.x
• NumPy
• SciPy
• Matplotlib (optional, for visualization)

Contributions are welcome! Please fork the repository and submit a pull request with your improvements.

This project is licensed under the MIT License. See the `LICENSE` file for details.

• Original PageRank algorithm by Larry Page and Sergey Brin. Research Paper: https://www.sciencedirect.com/science/article/pii/S016975529800110X
• Inspired by the Markov Decision Process theory.

If you have any questions or suggestions, feel free to contact me at [email protected].