This project explores the youth unemployment trends in South Africa using data from ILOSTAT. The focus is on the age group 15–24, and the final insights are visualized in Power BI.
To clean, analyze, and visualize youth unemployment in South Africa — and understand how unemployment rates vary across gender and time.
- Source: ILOSTAT
- Dataset: Unemployment rate by country, source, age group, gender, and year.
- Subset Used: Only data for South Africa, filtered to include Youth (15–24).
- Google Colab (Python / Pandas for data cleaning & EDA)
- Power BI (for storytelling & interactive visuals)
- GitHub (project hosting & documentation)
- Filtered for
ref_area.label = South Africa - Filtered
classif1.label = Age (Youth, adults): 15–24 - Encoded gender labels (
0 = Female,1 = Male,2 = Total) - Converted time to datetime and created year columns
- Dropped unused columns with heavy missing values
- Line Chart: Gender-based trends from 2000–2024
- Card: To depict the unemployment rate average
- Decomposition Tre: To breakdown, and get a detailed insights on the factors that influence unemployment, by year and gender
- Tooltips: Custom insights when hovering on visuals
- Youth unemployment in South Africa remains consistently high, especially among females.
- The unemployment rate spiked after 2020, potentially reflecting economic challenges due to COVID-19.
- Gender disparity is visible — with females often reporting higher unemployment rates.
Feel free to fork the repo, raise issues, or any suggestions!