🌍 GDP vs Life Expectancy Dashboard

This interactive Dash application visualizes the relationship between GDP per capita and life expectancy for various countries in the year 2007. It aims to help users explore how economic output relates to health outcomes globally, using a clean and intuitive scatter plot enriched with contextual information like population size and continent.

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📊 Features

• 🧮 Interactive scatter plot of GDP per capita vs life expectancy
• 🌐 Color-coded continents for regional comparison
• 🧠 Bubble size represents population size
• 🔍 Hover to see country-specific details
• 🧭 Dynamic and intuitive layout using Plotly Dash
• ✅ Highlights trends, outliers, and regional patterns

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🛠️ Built With

• Dash – for building the interactive dashboard
• Plotly – for advanced, interactive plotting
• Pandas – for data manipulation
• Python – core logic and server-side scripting
• CSV Data – containing country-level indicators from 2007

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🚀 Getting Started

📦 Prerequisites

Make sure you have Python 3.7+ installed. Then install the required libraries:

pip install dash pandas plotly

🧪 Run the App

Clone the repository and run the app:

git clone https://github.com/yourusername/my_dash_app.git
cd my_dash_app
python app.py

Then open your browser and navigate to:

http://127.0.0.1:8050/

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📂 Project Structure

my_dash_app/
│
├── app.py               # Main Dash application
├── data/
│   └── gapminder2007.csv  # Dataset used for visualization
├── assets/
│   └── style.css        # (Optional) Custom CSS styles
├── README.md            # Project documentation
└── requirements.txt     # Dependencies

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📈 Example Visualization

A sample output of the dashboard:

• X-axis: GDP per Capita
• Y-axis: Life Expectancy
• Color: Continent
• Size: Population

This layout allows easy spotting of clusters and anomalies, such as high-income countries with unexpectedly low life expectancy or large-population countries with low GDP but high life expectancy.

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📄 Dataset Source

The data is sourced from the Gapminder Foundation, representing socio-economic statistics for the year 2007.

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🔮 Future Improvements

• Year slider to explore data across multiple years
• Filtering by continent or income group
• Export data or chart snapshots
• Mobile-friendly layout

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🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to improve the app.

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📄 License

This project is licensed under the MIT License.

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👨‍💻 Author

Obakeng Mdonyelwa 🔗 GitHub 🔗 LinkedIn