🌍 Gapminder Data Exploration and Visualization

This project explores the Gapminder dataset, which contains socioeconomic indicators such as life expectancy, GDP per capita, and population for countries across different continents between 1952 and 2007.

The goal is to practice data analysis and visualization techniques in Python using libraries like pandas and seaborn.

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📂 Dataset Information

• Number of observations: 1,704
• Number of variables: 6
• Variables:
  • country: Name of the country
  • continent: Continent of the country
  • year: Year of observation
  • lifeExp: Life expectancy at birth
  • pop: Population
  • gdpPercap: GDP per capita in constant PPP dollars

📊 Source: Gapminder.org (Free data from the World Bank, CC-BY License)

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🔹 Project Highlights

• Configured visualization aesthetics using Seaborn.
• Explored data distributions and summary statistics.
• Created count plots, histograms, violin plots, scatter plots, and line plots.
• Analyzed relationships between:
  • Life Expectancy and GDP
  • Trends in life expectancy by country and continent
  • Evolution of socioeconomic indicators over time

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🛠️ Technologies

• Python
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn

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📌 Exercises Included

1. Distribution of Life Expectancy by countries in Europe.
2. Evolution of Life Expectancy in Spain, France, Germany, and Italy.
3. Relationship between Life Expectancy and GDP in selected European countries.
4. Distribution of Life Expectancy by continent.
5. Evolution of Life Expectancy by continent (line plots).
6. Regression analysis of Life Expectancy trends by continent.