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International Debt Analysis

Power BI POSTGRESQL Microsoft Excel Canva Visual Studio Code Markdown Microsoft Office Microsoft Word GitHub

Welcome to my International Debt Analysis Portfolio Project! In this project, I explore and analyze the complex landscape of international debt, aiming to gain insights into the debt trends and their impact on economies worldwide. Through meticulous data analysis, I aim to shed light on crucial aspects of global debt and provide meaningful implications for policymakers, economists, and investors.

Project Scope

The focus of this project is to delve into the realm of international debt analysis and understand its dynamics in a specific context. By narrowing down the scope, I aim to provide in-depth insights that resonate with the complexities and challenges faced by the selected region/country.

Reports

Interactive report on Novapro

Interactive report on Tushar-Aggarwal.com

SQL Queries Report

Project Structure

├── LICENSE
├── README.md          <- README for using this project.
├── query              <- Code of the DB creation and queries.
│   │
│   └── pizza_sales_db.sql       <- DB creation.
│   └── query.sql                <- Final queries.

├── reports            <- Folder containing the final reports/results of this project.
│   │
│   └── Pizza_Sales_Report.pdf   <- Final analysis report in PDF.
│   └── query_report.pdf         <- Final query report in PDF for verifying data.
│   
├── src                <- Source for this project.
    │
    ├── data           <- Datasets used and collected for this project.
    │   
    ├── pizza_sales_images       <- Additional images for Dashboards.
    │
    ├── data_dictionary.csv      <- Data Dictionary for the dataset.

Data Collection

To ensure the reliability and accuracy of my analysis, I sourced the data from reputable organizations, including the World Bank, International Monetary Fund (IMF), and national central banks. The dataset encompasses a range of variables, such as debt-to-GDP ratios, debt composition, interest rates, and repayment schedules.

Dataset Overview

Data Cleaning and Preprocessing

Raw data is seldom ready for analysis, and this project was no exception. Before diving into the exploration, I meticulously cleaned and preprocessed the data, addressing missing values, outliers, and inconsistencies. The resulting dataset lays the foundation for robust and meaningful analysis.

Exploratory Data Analysis (EDA)

EDA is an essential step in uncovering patterns and relationships within the data. Through visualizations, graphs, and statistical summaries, I reveal significant trends and correlations, offering a comprehensive understanding of the international debt landscape.

Hypothesis Testing (Optional)

In some sections of the project, I formulated specific hypotheses and conducted statistical tests to validate or refute them. This process allowed me to derive actionable insights, which further enrich the depth of my analysis.

Predictive Models (Optional)

In select areas of this project, I employed machine learning algorithms such as regression, time series analysis, or classification to build predictive models. These models were instrumental in forecasting future debt trends and assessing the risk of debt default, providing valuable insights for decision-makers.

Insights and Conclusions

Based on the findings from the analysis, I present a series of critical insights and conclusions. These conclusions are designed to offer a deeper understanding of the international debt scenario and its implications for various stakeholders.

Visualizations and Reports

The project's visual appeal is of utmost importance, and I present the findings through interactive visualizations, charts, and dashboards. In addition, I have prepared a comprehensive report that documents every step of my analysis, making it easier for others to comprehend and reproduce the process.

Challenges and Limitations

This portfolio project is not without its challenges and limitations. I provide an honest account of the hurdles faced during the analysis, including data limitations, technical complexities, and potential areas for improvement.

Share Your Thoughts

I am thrilled to share my International Debt Analysis Portfolio Project with you. Your feedback, suggestions, and insights are highly valuable to me. Please feel free to explore the project and connect with me for any questions or collaborations.

Key Questions Explored

  1. TWho do we owe all this money to?
  2. Can a debt load of three times GDP ever be sustainable?
  3. Is ther any responsible way to reduce this debt?
  4. What could go wrong if we can't

Summary of Findings

Debt at different levels

Individual Level

  • At individual level, debt is where someone takes out a loan from someone else and pays it over time.

National Level

  • At National level, debt is in relation to Debt to GDP Ratio,

Global Level

  • At Global level, debt is also owed to global economy, that is every liablity for a Government, Company, Bank or an Individual, is a Asset to another Government, Company, Bank or an Individual.

  • More global debt is benificial if it is used into projects that will produce more value in the global economy in long run.

Author

Contact me!

If you have any questions, suggestions, or just want to say hello, you can reach out to us at Tushar Aggarwal. We would love to hear from you!