https://github.com/its-kanii/population-data-analysis

https://github.com/its-kanii/population-data-analysis

Last synced: 6 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/its-kanii/population-data-analysis
• Owner: its-kanii
• Created: 2024-10-28T10:48:53.000Z (20 days ago)
• Default Branch: main
• Last Pushed: 2024-11-02T06:40:24.000Z (15 days ago)
• Last Synced: 2024-11-02T07:21:13.270Z (15 days ago)
• Language: Jupyter Notebook
• Size: 55.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Population Data Analysis

This project analyzes population data across various countries and continents over multiple years. Using Python libraries, we explore trends in population growth, distribution, density, and more. This README provides an overview of the charts and plots generated to visualize the dataset.

## Dataset Columns

The dataset contains the following columns:

- **rank**: Ranking of the country by population.

- **cca3**: Country code (3-letter ISO code).

- **country**: Name of the country.

- **continent**: Continent where the country is located.

- **Yearly Population**: Population values for years 1970, 1980, 1990, 2000, 2010, 2015, 2020, 2022, and 2023.

- **area (km²)**: Total area of the country in square kilometers.

- **density (km²)**: Population density per square kilometer.

- **growth rate**: Annual population growth rate (as a decimal).

- **world percentage**: Country’s population as a percentage of the world population (as a decimal).

## Data Visualization

### 1. Population Growth Over Time (Line Plot)

Visualizes the population growth for selected countries from 1970 to 2023, highlighting trends and changes in population over the years.

### 2. Population Distribution by Continent (Bar Plot)

Shows the population distribution across different continents for 2023, illustrating how the world population is concentrated in specific regions.

### 3. Population Density vs Area (Scatter Plot)

Illustrates the relationship between population density and area for each country, providing insights into how densely populated countries compare by land area.

### 4. Top Countries by Population (Horizontal Bar Plot)

Displays the top 10 most populous countries in 2023, highlighting countries with the largest populations.

### 5. Count of Countries by Continent (Bar Chart)

Shows the count of countries by continent, providing an overview of how countries are distributed across continents.

### 6. Distribution of Population (Histogram)

Displays the distribution of population in 2023, showing how population sizes vary across countries.

### 7. Correlation Analysis (Heatmap)

Represents the correlation between population-related metrics such as area, density, growth rate, and population across various years.

## Requirements

- Python 3.x

- Pandas

- Matplotlib

- Seaborn

To install the required libraries:

```bash

pip install pandas matplotlib seaborn

```

## Usage

Each visualization provides insights into different aspects of global population data. To explore the dataset, run the provided code snippets to generate each chart.

--- 

---

## License

This project is open-source and available under the MIT License.

## Acknowledgments

Thanks to [Kaggle](https://www.kaggle.com/) for providing an interactive environment for data analysis.