Data analysis project exploring unemployment trends across African countries.
This project presents a data-driven analysis of unemployment patterns across Africa, using 2024 country-level data to identify key drivers, demographic disparities, and structural labour market challenges. The insights are visualized in an interactive Power BI dashboard.
Objectives:
The goals of this project are to:
•Analyse unemployment trends across Africa
•Identify regional and demographic patterns in unemployment
•Determine key drivers of unemployment
•Identify gaps between workforce skills and employer demands
•Use tools to extract patterns and generate insights
Data Description:
•Source: The dataset used in this analysis is based on 2024 country-level unemployment data sourced from The Global Economy (
theglobaleconomy.com), which provides country-level unemployment rates across African nations for the year 2024.
•Table: AfricanUnemployment
•Rows: 500
•Columns:
o Country
o Region
o Age group
o Gender
o Education Level
o Area (urban/rural)
o Marital Status
o Years of Experience
o Internet access
o Reason for Unemployment
o Unemployment Rate
Data Cleaning and Preparation
•Standardization of variable names and formats
•Categorization of unemployment reasons
•Validation of data types for analysis
•New column created: Experience Level (Entry Level, Mid level, Senior)
Analysis and Metrics
•Regional Trends: The analysis reveals significant regional disparities in unemployment rates. Southern Africa has the highest average unemployment rate at 17.9%, while West Africa records the lowest at 5.2%. This suggests that structural economic differences and varying levels of industrial development influence employment opportunities across regions.
•Age Group Analysis :Youth aged 15–24 experience the highest unemployment rate at 20%, making them the most vulnerable group. This indicates that first-time job seekers face structural barriers to entry into the labour market.
•Education Level Analysis: An unexpected finding is that i …