PREDICTIVE MODELLING OF SOCIO-ECONOMIC TRENDS USING MACHINE LEARNING AND THEIR IMPLICATIONS ON POLICY MAKING
Keywords:
Predictive Modeling, Machine Learning, Trends, Policy Making, Random Forest, Recurrent Neural Networks, Data-Driven Decision Making, Economic ForecastingAbstract
The application of machine learning (ML) to forecast socio-economic trends represents a transformative tool for policy-making, offering governments a means to anticipate and address societal shifts with unprecedented accuracy. This study explores the use of various ML algorithms, including Random Forests and Recurrent Neural Networks (RNNs), to predict key socio-economic indicators such as GDP, unemployment, and inflation. Leveraging large datasets from global economic databases, our approach not only enhances predictive precision but also enables a comprehensive understanding of interrelated variables critical to effective policy decisions. Results demonstrate that advanced ML models outperform traditional linear models, capturing complex patterns and improving forecast reliability. These findings underscore ML's potential to foster proactive, data-driven policy development, though challenges remain regarding data accessibility and model interpretability. This research highlights the promising role of ML in the socio-economic domain, providing insights into the future of policy-making in a data-centric world.
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