Predictive Analytics for Economic Recession Forecasting using Machine Learning

Economic recessions have wide-reaching international impacts, affecting employment rates, financial markets, andgovernment policies. Accurate forecasting of economic downturns is important for policymakers, firms, and financialinstitutions to come up with proper countermeasures. This research explores the use of predictive analytics combinedwith machine learning techniques for forecasting economic recessions. A few macroeconomic indicators—e.g., GDPgrowth rate, unemployment rate, interest rate, and consumer confidence index—are used for training and testing somesupervised learning models like Logistic Regression, Random Forest, Support Vector Machines, and GradientBoosting. These models are evaluated based on accuracy, precision, recall, and ROC-AUC value. Feature selectionand dimensionality reduction techniques are applied for enhancing the interpretability and performance of models. Theresults indicate that machine learning models, particularly ensemble methods, are capable of detecting subtle patternsand providing early warning signals of future recessions. The paper demonstrates the potential of data-drivenapproaches in economic forecasting and presents directions towards real-time data aggregation for dynamic andadaptive recession forecasting.