🌍 Exploring Global Economic Trends with World Bank Data 📊

This project explores long-term trends in global economic indicators using data from the World Bank. The main focus is on GDP and GDP per capita, analyzed across different income groups over time.

🧠 Objective

To analyze how economic indicators like GDP and GDP per capita have evolved across different income groups (low, lower-middle, upper-middle, and high income), using real-world time series data.

🔧 What I Did

• 🔹 Data Cleaning & Preprocessing

  • Handled inconsistencies and missing values
  • Standardized column names and data types

• 🔹 Missing Value Treatment

  • Applied forward fill and backward fill techniques to address missing data

• 🔹 Data Visualization

  • Created clear and informative plots using Matplotlib to visualize:
    • Long-term trends in GDP by income group
    • Comparative changes in GDP per capita
    • Economic disparities over time

📈 Key Insights

Simple visualizations can be incredibly powerful for storytelling. Watching the economic gap between income groups widen or narrow over time was both insightful and eye-opening.

💡 Takeaways

Projects like this remind me why I enjoy working with data. Every dataset holds a story — you just have to uncover it.

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🛠️ Tech Stack

• Python
• Pandas
• Matplotlib
• Jupyter Notebook

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📁 Project Structure

world-bank-economic-trends/ ├── data/ │ └── world_bank_data.csv ├── notebooks/ │ └── global_trends_analysis.ipynb ├── visualizations/ │ └── gdp_per_capita_by_income_group.png ├── README.md

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🙌 Let's Connect

Have you worked with similar datasets or done global economic analysis? I’d love to learn from your experience or get your feedback on how to improve this project.

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📌 Hashtags

#DataScience #Python #WorldBankData #DataVisualization #Pandas #Matplotlib #LearningJourney