AlgorithmVisualizer

Algorithm Visualizer

Algorithm Visualizer

Live Demo

Visit: https://aagamaj.github.io/AlgorithmVisualizer/

Description

Algorithm Visualizer is a web application that allows users to visualize various algorithms in action. It provides an interactive interface to understand how algorithms work, step by step. Users can observe the algorithm’s execution and analyze its performance on different datasets. The visualizations aim to help users grasp complex algorithms more effectively and intuitively.

Table of Contents

Features

Supported Algorithms

Getting Started

Prerequisites

Make sure, git is installed in your System.

Installation

  1. Open a terminal or command prompt on your computer.

  2. Navigate to the directory where you want to clone the repository. You can use the cd (Change Directory) command to move to the desired location. For example, to navigate to your Documents folder on Windows, use:

cd C:\Users\YourUsername\Documents

On macOS or Linux, you can use:

cd /Users/YourUsername/Documents
  1. Once you are in the desired directory, use the git clone command followed by the repository URL. The repository URL can be found on the GitHub repository page, usually under the “Code” button with a “Clone” option.

  2. Type the following command in your Terminal to clone the Repository

git clone https://github.com/AagamAJ/AlgorithmVisualizer.git
  1. Press Enter to execute the git clone command. Git will start downloading the repository to your local machine.

  2. Once the cloning process is complete, you will see a new folder in your current directory with the repository name. You can navigate into this folder to access the files and work on the project.

That’s it! You have now successfully cloned the GitHub repository to your local machine using the command line. You can make changes to the files, commit them, and push them back to the remote repository when you are ready.

Usage

The “Algorithm Visualizer” project allows users to:

  1. Understand how algorithms work through step-by-step visualization.
  2. Compare the performance of different algorithms on various datasets.
  3. Debug and test algorithm implementations.
  4. Prepare for technical interviews and improve problem-solving skills.
  5. Use it as a teaching aid and research tool for algorithm analysis.

Acknowledgements

I took inspirations from the following sources for some of the segments.

Contributing

Contributions are welcome! If you find any issues or have ideas to improve the Project, feel free to open an issue or create a pull request.

Contact

For any questions or inquiries, please contact me through my Email- aagamaj1212@gmail.com (Aagam Jain).