This project involves a short exploratory data analysis (EDA) on life expectancy data. The goal is to uncover patterns and gain insights using SQL and Python for data analysis.
Understanding life expectancy trends can help in assessing the health and development status of different countries. This project analyzes various factors affecting life expectancy through EDA.
Description: The dataset contains multiple records, including features such as country, year, and factors related to health and socio-economic status.
SQL: Used for data querying. Python: Used for additional data analysis and visualization.
The analysis explores:
- Correlation between life expectancy and socio-economic indicators like GDP, vaccination rates, etc.
- Comparisons across different countries or regions.
- Effect of schooling
📈 Higher GDP per capita often correlates with increased life expectancy.
🫄🏼 Higher GDP countries tend to have higher average BMI.
👩🏼🎓 Longer schooling duration often correlates with higher GDP.
This project is summarized in a short presentation.