https://github.com/munjpatel/bayesian-networks-a-practical-guide

This repository demonstrates the application of Bayesian Networks for modeling relationships between variables, enabling data-driven predictions and decision-making. It includes real-world use cases such as disease prediction, loan default classification, weather forecasting, and stock price forecasting.
https://github.com/munjpatel/bayesian-networks-a-practical-guide

bayesian-network disease-prediction loan-default-prediction stock-price-prediction weather-forecast

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This repository demonstrates the application of Bayesian Networks for modeling relationships between variables, enabling data-driven predictions and decision-making. It includes real-world use cases such as disease prediction, loan default classification, weather forecasting, and stock price forecasting.

• Host: GitHub
• URL: https://github.com/munjpatel/bayesian-networks-a-practical-guide
• Owner: MunjPatel
• License: mit
• Created: 2024-11-22T10:56:31.000Z (11 months ago)
• Default Branch: dev
• Last Pushed: 2024-11-22T11:11:12.000Z (11 months ago)
• Last Synced: 2025-02-01T08:28:24.731Z (8 months ago)
• Topics: bayesian-network, disease-prediction, loan-default-prediction, stock-price-prediction, weather-forecast
• Language: Python
• Homepage: https://medium.com/@patelmunj2011/bayesian-networks-a-practical-guide-4edf97e4e6d4
• Size: 11.7 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Bayesian Networks: A Practical Guide

This repository contains the code accompanying the Medium article: [Bayesian Networks: A Practical Guide](https://medium.com/@patelmunj2011/bayesian-networks-a-practical-guide-4edf97e4e6d4).

## Overview

The code demonstrates the application of Bayesian inference and probabilistic graphical modeling techniques to real-world problems in prediction and decision-making. By leveraging Bayesian Networks to model relationships between variables, the code showcases how to simplify complex systems, quantify uncertainty, and gain actionable insights. These techniques are pivotal in areas like financial modeling, healthcare diagnostics, and weather forecasting, where understanding dependencies and making data-driven predictions are critical.

## Repository Structure

- `requirements.txt`: List of all the dependencies for the project.

- `disease_prediction.py`: Python script for implementing Bayesian Network for disease classification.

- `loan_default_prediction.py`: Python script for implementing Bayesian Network for loan default classification.

- `weather_forecasting.py`: Python script for implementing Bayesian Network for rain classification.

- `stock_price_forecasting.py`: Python script for implementing Bayesian Network for stock price forecasting.

## Usage

The python scripts provide a comprehensive walkthrough of the analysis. Each script is self-contained and can be executed independently.

## Contributing

Contributions are welcome! If you have suggestions or improvements, please open an issue or submit a pull request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

Special thanks to the readers of the accompanying Medium article for their feedback and support.

For a detailed explanation of the concepts and methodologies used, please refer to the original [article](https://medium.com/@patelmunj2011/bayesian-networks-a-practical-guide-4edf97e4e6d4)