International Debt Data Analysis

Introduction

Welcome to the International Debt Data Analysis repository! This project focuses on analyzing international debt data provided by The World Bank. Through SQL queries and data exploration, we aim to gain insights into global debt trends, identifying key countries, and understanding debt composition.

Objective

The primary objectives of this analysis are:

• Calculate the total accumulated debt across all countries.
• Identify the country with the highest debt and its corresponding amount.
• Explore the average debt amount across different debt indicators.

Data Source

The dataset used for this analysis is sourced from The World Bank's international debt statistics. It contains information about debt amounts in USD for various developing countries.

Methodology

The analysis involves:

1. Connecting to the international_debt database.
2. Running SQL queries to extract relevant information.
3. Calculating total debt, identifying the highest debtor, and computing average debt.
4. Visualizing key findings using graphs and charts.

Repository Structure

• queries/: Contains SQL queries for data analysis.
• results/: Contains output files and visualizations.
• images/: Store images used in the README.

Additionally, the main Jupyter Notebook file for the project is located at analysis.ipynb. This notebook contains all the commands and explanations for the data analysis process, along with the dataset used.

Feel free to explore and navigate through the different directories to get a comprehensive understanding of the project structure.

Usage

To replicate this analysis on your local machine:

1. Clone this repository: git clone https://github.com/yourusername/international-debt-analysis.git
2. Set up a suitable SQL database.
3. Import the dataset into your database.
4. Run the SQL queries from the queries/ directory.
5. Explore the results in the results/ directory.

Results

Findings and visualizations from the analysis are available in the results/ directory.

Conclusion

This project offers valuable insights into international debt trends and patterns. By leveraging SQL and data visualization, we shed light on the global debt landscape.

Contributing

Contributions are welcome! If you have suggestions, improvements, or new analyses, feel free to open issues or pull requests.

License

This project is licensed under the MIT License.

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