MODELLING SKILL DEMAND TRENDS IN THE AFRICAN LABOUR MARKET THROUGH MACHINE LEARNING ALGORITHMS

The rapid digital transformation across global industries has intensified the demand for modern technical skills, exposing significant skill gaps within African labour markets. Traditional labour forecasting methods, which rely on surveys and static reports, struggle to capture evolving workforce trends and often fail to provide timely, data-driven insights. This limitation motivates the adoption of machine learning-based analytical models capable of identifying emerging skill patterns from historical job market data. This study aims to develop and evaluate a predictive system for forecasting skill demand using structured datasets collected from African job portals. Specifically, the research applies Linear Regression as the core forecasting model to estimate future demand trends for key digital skills. The methodology involves a complete data preprocessing pipeline consisting of cleaning, normalization, and restructuring job-skill frequency data into model-readable formats. A curated dataset covering multiple skills across four years was used, and the system was implemented using Python and Streamlit for interactive visualization. Model performance was assessed using accuracy, trend-direction consistency, and graphical evaluation metrics derived from observed versus predicted values. Results show that the Linear Regression model accurately captured general growth patterns for high-demand digital skills such as Python and Data Analysis, achieving strong alignment between historical trajectories and forecasted values. The deployed system demonstrated stability, fast response time, and ease of use, enabling real-time skill trend visualization and one-step-ahead forecasting. The study confirms the potential of machine learning approaches for supporting labour market analysis in Africa. However, limitations include restricted dataset size, reliance on numerical trend features, and absence of deep learning models such as LSTM, which may better capture complex temporal dependencies. Future research should incorporate larger datasets, integrate natural language processing for extracting skills from job descriptions, and extend the forecasting engine to more advanced time-series models for improved prediction accuracy and adaptability.