mkhabele-ef/sa-health-dashboard

Python data analysis of South African causes of death by province. # South Africa Provincial Health Burden Dashboard A Python data analysis project exploring causes of death across all 9 South African provinces, built using pandas, matplotlib and seaborn. ## Why this project Raw death counts are misleading a province with more people will always have more deaths. This analysis normalizes data per 100,000 population to reveal the true disease burden in each province. ## Tools used - Python - pandas - matplotlib - seaborn ## Dashboard ## Key insights - **KwaZulu-Natal** has the highest HIV/AIDS death rate per 100k population - not Gauteng, despite Gauteng having more people - **Free State** shows surprisingly high TB deaths per 100k, a result that challenges the assumption that larger provinces carry the biggest burden - **Free State** also carries the heaviest heart disease burden per 100k in the country, significantly outweighing diabetes - suggesting cardiovascular disease is the dominant non-communicable disease threat there ## Author Ehleketani Mkhabele - BSc Human Physiology, Genetics & Psychology (University of Pretoria)