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https://github.com/smahala02/monte-carlo-simulation
A comprehensive tutorial on Monte Carlo Simulation using Python, demonstrating how random sampling and probabilistic models can be used for various real-world applications, including finance, physics, and engineering.
https://github.com/smahala02/monte-carlo-simulation
data-science machine-learning monte-carlo-simulation probability python random-sampling
Last synced: 8 days ago
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A comprehensive tutorial on Monte Carlo Simulation using Python, demonstrating how random sampling and probabilistic models can be used for various real-world applications, including finance, physics, and engineering.
- Host: GitHub
- URL: https://github.com/smahala02/monte-carlo-simulation
- Owner: smahala02
- License: mit
- Created: 2024-09-07T13:24:03.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-07T13:32:22.000Z (4 months ago)
- Last Synced: 2024-11-08T01:57:07.557Z (about 2 months ago)
- Topics: data-science, machine-learning, monte-carlo-simulation, probability, python, random-sampling
- Language: Jupyter Notebook
- Homepage:
- Size: 149 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Monte Carlo Simulation
## Overview
This repository contains a detailed tutorial on Monte Carlo Simulation, a computational technique that utilizes random sampling to obtain numerical results. The **Jupyter Notebook** in this repository explains the basic concepts, the process of simulation, and provides real-world examples to help you understand the technique.
Monte Carlo simulations are widely used in areas such as finance, engineering, physics, and risk assessment, allowing for predictions and analysis based on probabilistic models.
## Contents
1. `Monte Carlo Simulation.ipynb` – The Jupyter Notebook containing the tutorial and example code.
2. `README.md` – Information about the repository and how to use it.
## Navigation
The following sections are covered in the Jupyter Notebook:
- **Introduction to Monte Carlo Simulation**: Brief overview of what Monte Carlo Simulation is and where it is applied.
- **Random Sampling**: Explanation of how random sampling is used in Monte Carlo methods.
- **Basic Simulation Example**: A step-by-step example demonstrating a simple Monte Carlo simulation.
- **Advanced Simulation Example**: A more complex simulation with applications to real-world scenarios.
- **Visualization**: Graphs and plots that illustrate the results of the simulations.
## Usage
### Prerequisites
To use this repository, you will need the following tools installed:
- [Python](https://www.python.org/downloads/)
- [Jupyter Notebook](https://jupyter.org/install)
- Python libraries: `numpy`, `matplotlib`, and `scipy` (install via `pip` if necessary).
### Installation Steps
1. Clone the repository to your local machine:
```bash
git clone https://github.com/smahala02/Monte-Carlo-Simulation.git
```
2. Navigate to the directory:
```bash
cd Monte-Carlo-Simulation
```
3. Launch Jupyter Notebook:
```bash
jupyter notebook "Monte Carlo Simulation.ipynb"
```
4. Run each cell in the notebook to execute the simulations and view the results.
## License
This project is licensed under the MIT License. Please see the `LICENSE` file for more information on permissions and usage.
## Contributing
Contributions are highly encouraged! If you would like to contribute to this project, please follow these steps:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Commit your changes (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Open a pull request, and we will review it.
If you find any bugs or have suggestions, feel free to open an issue or submit a pull request!
## Author
- [Your Name](https://github.com/yourusername)