Conducted EDA on health and demographic data (2014-2015) from the Global Health Observatory Data Repository using Python (pandas, matplotlib, seaborn) in a vscode environment. Notable achievements include:

• Cleaned and standardized data through mean imputation and whitespace removal.

• Identified nations with the highest/lowest youth populations, revealing global health patterns.

• Explored correlations between fertility, literacy, life expectancy, and income across regions.

• Utilized Git for version control, ensuring transparency and collaboration.

Insights: The dataset provided valuable insights into global health and demographics, highlighting variations and trends across regions. Notable relationships include the inverse correlation between fertility and income and the positive association between literacy and life expectancy.

For detailed insights, visit my github: https://github.com/Itumeleng-data-analyst/EDA-HEALTH-DATA/blob/master/NOTEBOOK/EDA.IPYNB