Abstract: The dynamic nature of unemployment rates presents a persistent challenge for policymakers and economists striving to maintain labor market stability. Fluctuations in employment levels are influenced by a multitude of factors, including economic shifts, policy changes, and global market conditions. This project introduces a predictive model designed to analyze unemployment trends using linear regression enhanced with recursive data analysis. By examining historical unemployment data, the model identifies critical patterns and key influencing factors, offering valuable insights into employment dynamics. The integration of recursive data handling allows the model to continuously update its predictions as new data becomes available, refining its accuracy over time. This adaptive approach ensures that the model remains responsive to evolving economic conditions, making it a reliable tool for labor market analysis. Through predictive insights, this system enables policymakers, economists, and other stakeholders to make informed, data- driven decisions aimed at mitigating unemployment.. Ultimately, this model serves as a robust analytical framework for understanding and managing employment trends in an ever changing economic landscape.
Keywords: Unemployment Prediction, Machine Learning, Random Forest, SVM, KNN, Data Analysis, Economic Stability, Workforce Management, Real-time Data, Predictive Modeling, Feature Engineering, Policy Formulation, NLP, Data Visualization.
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DOI:
10.17148/IJARCCE.2025.14578
