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https://github.com/thecoderpinar/worldpopulationanalysis2024

World Population Analysis 2024: An In-Depth Exploration of Urban and Rural Populations and Infrastructure Accessibility
https://github.com/thecoderpinar/worldpopulationanalysis2024

data-analysis data-science economic-indicators machine-learning population-growth prophet-forecasting

Last synced: 18 days ago
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World Population Analysis 2024: An In-Depth Exploration of Urban and Rural Populations and Infrastructure Accessibility

Host: GitHub
URL: https://github.com/thecoderpinar/worldpopulationanalysis2024
Owner: ThecoderPinar
License: mit
Created: 2024-07-07T14:54:26.000Z (6 months ago)
Default Branch: main
Last Pushed: 2024-07-07T15:04:28.000Z (6 months ago)
Last Synced: 2024-07-08T16:25:41.965Z (6 months ago)
Topics: data-analysis, data-science, economic-indicators, machine-learning, population-growth, prophet-forecasting
Language: Jupyter Notebook
Homepage:
Size: 1.32 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # 🌍 WorldPopulationAnalysis2024 🌍

## Description 📋

**WorldPopulationAnalysis2024** is a comprehensive project aimed at analyzing the world population in 2024. It focuses on comparing urban and rural areas regarding population density, growth rates, and access to essential infrastructure services such as electricity, clean water, healthcare, and education. The project leverages advanced data visualization techniques and machine learning models to provide valuable insights into the correlation between population distribution and economic indicators.

## Features ✨

- **Urban vs. Rural Population Analysis** 🏙️🌾: Compare the population sizes and densities of urban and rural areas across various countries.

- **Infrastructure Accessibility** ⚡🚰🏥📚: Analyze access to electricity, clean water, healthcare, and education in urban and rural regions.

- **Economic Indicators Correlation** 📊: Study the correlation between population distribution and economic indicators such as GDP, unemployment rates, and education levels.

- **Data Visualization** 📈: Utilize advanced visualization tools to present data insights in an understandable manner.

- **Machine Learning Models** 🤖: Implement regression models to predict future trends and analyze relationships between variables.

## Technologies Used 🛠️

- **Python**

- **Pandas**

- **Matplotlib**

- **Seaborn**

- **Plotly**

- **Folium**

- **Scikit-learn**

- **Prophet**

## Dataset Sources 📚

- **[World Population by Country 2024](https://www.kaggle.com/datasets)**: Comprehensive population data for each country.

- **[Country Coordinates World](https://www.kaggle.com/datasets)**: Geographic coordinates for each country.

- **Economic and Infrastructure Data**: Data on GDP, unemployment rates, education levels, and infrastructure access.

## Getting Started 🚀

### Prerequisites

- Python 3.11

- Required Python libraries (specified in `requirements.txt`)

### Installation

1. Clone the repository:

    ```bash

    git clone https://github.com/yourusername/WorldPopulationAnalysis2024.git

    ```

2. Navigate to the project directory:

    ```bash

    cd WorldPopulationAnalysis2024

    ```

3. Install the required packages:

    ```bash

    pip install -r requirements.txt

    ```

### Usage

1. Run the Jupyter Notebook:

    ```bash

    jupyter notebook

    ```

2. Open `world_population_analysis.ipynb` to explore the analysis and visualizations.

## Results 🌟

The project provides a comprehensive analysis of how population distribution affects infrastructure access and economic indicators. Key findings include differences in access to essential services between urban and rural areas and the impact of these differences on economic outcomes.

## Detailed Findings 🕵️‍♂️

### Urban vs. Rural Population 🏙️🌾

| Region | Country         | Population 2024 |

|--------|-----------------|-----------------|

| Urban  | India           | 500,000,000     |

| Urban  | China           | 800,000,000     |

| Urban  | United States   | 250,000,000     |

| Urban  | Indonesia       | 150,000,000     |

| Urban  | Pakistan        | 90,000,000      |

| Urban  | Turkey          | 50,000,000      |

| Rural  | India           | 941,719,852     |

| Rural  | China           | 625,178,782     |

| Rural  | United States   | 91,814,420      |

| Rural  | Indonesia       | 129,798,049     |

| Rural  | Pakistan        | 155,209,815     |

| Rural  | Turkey          | 38,510,876      |

### Infrastructure Accessibility 📊

| Service                   | Urban (%) | Rural (%) |

|---------------------------|-----------|-----------|

| Electricity Access        | 96.67     | 75.00     |

| Clean Water Access        | 92.83     | 68.33     |

| Healthcare Access         | 88.00     | 55.00     |

| Education Access          | 87.50     | 62.50     |

### Economic Indicators 💹

| Indicator                 | Correlation (Urban) | Correlation (Rural) |

|---------------------------|---------------------|---------------------|

| GDP (Billion USD)         | 0.79                | 0.79                |

| Unemployment Rate (%)     | -0.34               | -0.34               |

| Education Level (Mean years) | 1.00             | 1.00                |

## Contributing 🤝

Contributions are welcome! Please fork the repository and create a pull request with your changes.

## License 📜

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact 📬

For more information, please contact [Pınar Topuz](mailto:[email protected]).