Prediction of Consumer Confidence Index Using Machine Learning Techniques

P S Keerthy and Dr. Sumera Aluru
Additional contact information
P S Keerthy: RV Institute of Management, Bengaluru
Dr. Sumera Aluru: Associate Professor, RV Institute of Management, Bengaluru

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 7, 487-497

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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijrsi/d ... -issue-7/487-497.pdf (application/pdf)
https://rsisinternational.org/journals/ijrsi/artic ... learning-techniques/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjc:journl:v:12:y:2025:i:67:p:487-497

Access Statistics for this article

International Journal of Research and Scientific Innovation is currently edited by Dr. Renu Malsaria

More articles in International Journal of Research and Scientific Innovation from International Journal of Research and Scientific Innovation (IJRSI)
Bibliographic data for series maintained by Dr. Renu Malsaria ().