How Macroeconomic Indicators Effect Consumer Spending Trends : ML Based Approach

Understanding consumer spending patterns is critical for economic stability and policy formulation. This study employs a machine learning-based approach to predict short-term consumer spending using macroeconomic indicators such as GDP growth, inflation, interest rates, exchange rates, unemployment, and consumer confidence.By integrating tree-based models (Random Forest, XGBoost) and temporal models (LSTM, BiLSTM with attention), we achieve high predictive accuracy, with XGBoost outperforming others (MAPE: 3.95 percent). Model interpretability using SHAP and attention mechanisms reveals that consumer confidence and GDP growth positively influence spending, while inflation, interest rates, and unemployment exert significant negative effects. Exchange rate depreciation also reduces consumption in import-reliant economies. Our findings align with established economic theory and past empirical studies, offering a robust, data-driven lens to forecast and understand consumption dynamics.The insights enable real-time, targeted, and proactive policy interventions—highlighting the promise of machine learning as a strategic tool for modern macroeconomic decision-making.