This project analyzes youth unemployment trends in South Africa using ILOSTAT data. It includes data cleaning in Python (Google Colab), exploratory insights, and an interactive Power BI dashboard focused on gender and time-based trends.
# πΏπ¦ South African Youth Unemployment Analysis
This project explores the **youth unemployment trends in South Africa** using data from **ILOSTAT**. The focus is on the age group **15β24**, and the final insights are visualized in **Power BI**.
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## Goal
To clean, analyze, and visualize youth unemployment in South Africa β and understand how unemployment rates vary across **gender and time**.
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## Data Source
- **Source**: ILOSTAT
- **Dataset**: Unemployment rate by country, source, age group, gender, and year.
- **Subset Used**: Only data for **South Africa**, filtered to include **Youth (15β24)**.
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## βοΈ Tools & Technologies
- Google Colab (Python / Pandas for data cleaning & EDA)
- Power BI (for storytelling & interactive visuals)
- GitHub (project hosting & documentation)
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## Data Cleaning Highlights
- Filtered for `ref_area.label = South Africa`
- Filtered `classif1.label = Age (Youth, adults): 15β24`
- Encoded gender labels (`0 = Female`, `1 = Male`, `2 = Total`)
- Converted time to datetime and created year columns
- Dropped unused columns with heavy missing values
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## Power BI Visuals
- **Line Chart**: Gender-based trends from 2000β2024
- **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
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## Insights
- 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 β with females often reporting **higher unemployment rates**.
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## Contact / Feedback
Feel free to fork the repo, raise issues, or any suggestions!
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