Promise-Chinonso/Comprehensive-Overview-of-Unemployment-in-Africa

This project takes a closer look at the unemployment rate in Africa, the trend over time and possible reasons for the continuous increase in the unemployment rate ## Comprehensive Overview of Unemployment in Africa (2021) #### Context One of the major challenges ravaging the African continent is the issue of unemployment amongst it's employable population. It may seem quite sad as the number of youths without jobs or valuable sources of income has hit an all time high. When compared to other continents of the world, one might be faced with answering the rhetorical question, why Aftica? The African continent is the second most populated continent in the world with its population numbering up to 1.4billion (approximately 18% of the world population) as at 2021. This project takes a closer look at the unemployment situation in Africa, its trends and potential impacting factors with a direct focus on the year 2021. #### Data Source(s) The data used for this analysis was provided by *10alytics* for its Global Hackathon, 2023. The data contains global records for the unemployment rate across genders, country codes information, the expenditure on education by the government of different countries, strategies in place for youth employment, population's access to electricity and number of firms existing in each country. The data spans across tens of years. #### Tool(s) Used I relied solely on Microsoft Excel for every step of this analysis. #### Processes - Connection to data source - Data cleaning and transformation - Analysis - Visualization and reporting #### Connecting To The Various Data Sources, Data Cleaning and Transformation Connecting to the 6 tables for this analysis was quite seamless as I leveraged the Power Query editor for connecting to the files containing these tables. Upon successful connection, the tables were individually profiled to get a breakdown of the value distributions and statistics. This process provided a clear view of the anomalies and inconsistencies within the data after which data cleaning and transformation followed. For the data cleaning, the tables were renamed and given better and mo …