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Prediction of Consumer Confidence Index Using Machine Learning Techniques

Author

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  • P S Keerthy

    (RV Institute of Management, Bengaluru)

  • Dr. Sumera Aluru

    (Associate Professor, RV Institute of Management, Bengaluru)

Abstract

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.

Suggested Citation

  • P S Keerthy & Dr. Sumera Aluru, 2025. "Prediction of Consumer Confidence Index Using Machine Learning Techniques," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(7), pages 487-497, July.
    • Handle: RePEc:bjc:journl:v:12:y:2025:i:67:p:487-497
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