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🎲 Monte Carlo Simulation

A web-based Monte Carlo Simulation application for modeling probability, uncertainty, and risk through repeated random sampling.

✨ Features

  • 🎲 Monte Carlo-based probabilistic simulation
  • 📊 Interactive simulation results
  • ⚡ Fast development environment powered by Vite
  • 🖥️ Browser-based interface
  • 📦 Simple Node.js/npm setup
  • 🚀 Production-ready build support

🛠️ Prerequisites

Before running the project, make sure you have the following installed:

  • Node.js
  • npm

You can download Node.js from Node.js.

🚀 Getting Started

Follow these steps to run the project locally.

1. Clone the Repository

git clone https://github.com/dharaarpan7/Monte-Carlo-Simulation.git

2. Navigate to the Project Directory

cd Monte-Carlo-Simulation

3. Install Dependencies

Install all required dependencies using npm:

npm install

This will read the project's package.json file and install the required packages into the node_modules directory.

4. Start the Development Server

Run the following command:

npm run dev

Vite will start the local development server.

5. Open the Application

After starting the development server, the terminal will display a local URL similar to:

http://localhost:5173

Open the URL in your browser to launch the Monte Carlo Simulation.

🏗️ Build for Production

To create an optimized production build, run:

npm run build

The production files will be generated in the dist directory.

Preview the Production Build

You can preview the production build locally with:

npm run preview

📁 Project Structure

A typical project structure looks like this:

Monte-Carlo-Simulation/
├── public/
├── src/
├── package.json
├── package-lock.json
├── index.html
└── vite.config.*

The exact structure may vary depending on the current implementation of the project.

🧪 Development

For development, start the Vite development server:

npm run dev

Changes made to the source code will generally be reflected automatically through Vite's hot-reload functionality.

🤝 Contributing

Contributions, bug reports, suggestions, and feature requests are welcome.

  1. Fork the repository.
  2. Create a new branch:
git checkout -b feature/your-feature
  1. Make your changes.
  2. Commit your changes:
git commit -m "Add your feature"
  1. Push the branch:
git push origin feature/your-feature
  1. Open a Pull Request.

📄 License

This project is available under the license specified in the repository.

👨‍💻 Author

Arpan Dhara

GitHub: @dharaarpan7


⭐ If you find this project useful, consider giving the repository a star!

About

Monte Carlo Simulation for modeling uncertainty, risk, and probabilistic outcomes through repeated random sampling. Explore distributions, estimate probabilities, and analyze potential outcomes with data-driven simulations.

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