Projects / Local Chapter Challenge

Predicting the Unemployment Rate in Kenya

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This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Machakos, Kenya.

The problem

The unemployment rate in Kenya has been a persistent challenge, particularly among the youth and urban populations, despite the country’s economic growth in recent years. This project aims to investigate the underlying factors contributing to the high unemployment rate in Kenya, analyze historical trends, and identify key drivers that can inform effective policies and interventions to promote job creation and economic development.

Some of the key questions to ask while doing this project will be:

  • What is the current unemployment rate in Kenya, and how has it evolved over the past decade or more?
  • What demographic groups are most affected by unemployment, and what are their specific challenges in the job market?
  • How do economic factors, such as GDP growth, inflation, and investment patterns influence the unemployment rate in Kenya?
  • What is the relationship between educational attainment and employment opportunities in Kenya?
  • Are there significant regional disparities in unemployment rates across different counties and regions in Kenya?
  • Can we identify potential predictors of unemployment and develop predictive models to understand future trends?

 

The primary objective of this project is to gain a comprehensive understanding of the unemployment rate in Kenya, analyzing its trends, causes, and effects. By leveraging data analysis, statistical modeling, and data visualization techniques, I aim to provide actionable insights to inform evidence-based policies and initiatives that can mitigate unemployment, enhance workforce participation, and create a conducive environment for economic prosperity in Kenya.

The goals

  • Analyse historical unemployment trends in Kenya over the past decade or more. 
  • Identify the demographic characteristics most affected by unemployment. 
  • Investigate the influence of economic factors, such as GDP growth and inflation, on unemployment rates. 
  • Examine the relationship between education levels and employment opportunities. e. Explore regional disparities in unemployment across different counties or regions in Kenya.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work

First Omdena Local Chapter Challenge?

Beginner-friendly, but also welcomes experts

Education-focused

Open-source

Duration: 4 to 8 weeks



Your Benefits

Address a significant real-world problem with your skills

Build your project portfolio

Access paid projects (as an Omdena Top Talent)

Get hired at top organizations



Requirements

Good English

Suitable for AI/ Data Science beginners but also more senior collaborators

Learning mindset



Application Form

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