Unemployment Trends in East Africa Analysis

This project aims to analyze and forecast unemployment trends in East African countries using historical data sourced from the International Labour Organization (ILO) and the World Bank. The analysis includes data preprocessing, exploratory data analysis (EDA), predictive modeling, and visualization to provide insights into unemployment rates and forecast future trends.

Installation

To run the analysis code, you need to have Python installed on your system along with the following libraries:

pip install pandas matplotlib seaborn scikit-learn numpy

Data Sources

The dataset used for this analysis is sourced from the World Bank's World Development Indicators, specifically focusing on the unemployment indicator provided by the International Labour Organization (ILO). You can access the data from the following link: World Bank - Unemployment Indicator

Analysis Steps

1. Data Cleaning and Preprocessing: The dataset undergoes cleaning and preprocessing to handle missing values, outliers, and format the data for analysis.

2. Exploratory Data Analysis (EDA): Various EDA techniques are employed to understand the distribution, trends, and relationships within the data. This includes visualizations such as line plots, histograms, and correlation matrices to uncover patterns and insights.

3. Predictive Modeling: A predictive model is built using machine learning algorithms to forecast future unemployment rates for East African countries. Linear regression is used as a simple model to demonstrate the forecasting process.

4. Data Visualization: Visualizations are created to effectively communicate insights from the analysis, including trends in unemployment rates over time, comparisons between countries, and forecasted trends for the future.

Results

The analysis provides valuable insights into unemployment trends in East African countries, highlighting patterns, relationships, and forecasts for future unemployment rates. Key findings include:

• Identification of countries with the highest and lowest unemployment rates.
• Analysis of unemployment trends over time and potential factors influencing these trends.
• Forecasted unemployment rates for the next 5 years using predictive modeling techniques.

Conclusion

The analysis of unemployment trends in East Africa offers valuable insights for policymakers, researchers, and stakeholders interested in understanding and addressing labor market challenges in the region. By leveraging historical data and predictive modeling, this analysis provides a foundation for informed decision-making and interventions to tackle unemployment issues in East Africa.

Contributions

Contributions to this project are welcome! If you have any suggestions, improvements, or would like to add new features, feel free to fork the repository and submit a pull request. Your contributions will be greatly appreciated in improving the analysis and making it more robust. Let's work together to enhance our understanding of unemployment trends in East Africa.