Prediction of Consumer Confidence Index Using Machine Learning Techniques

The Consumer Confidence Index (CCI) is a crucial economic indicator that reflects the optimism or pessimism of consumers regarding their expected financial situation and the overall economic environment. Accurate forecasting of the CCI enables policymakers, investors, and businesses to make well-informed decisions. This project explores the use of Machine Learning (ML) techniques to predict the future values of the Consumer Confidence Index based on historical economic data. The study uses a variety of macroeconomic indicators such as inflation, unemployment rates, GDP growth, interest rates, and stock market indices, which are known to influence consumer sentiments. By employing supervised machine learning models like Linear Regression, Random Forest Regressor, and Support Vector Regression (SVR), the study aims to determine the most effective algorithm for forecasting the CCI with high accuracy. The model’s performance is evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) values. The project demonstrates that machine learning can provide more dynamic and responsive forecasting compared to traditional statistical models. It contributes to the emerging intersection of behavioral economics and artificial intelligence by providing a predictive framework for analyzing consumer expectations.