Welcome to the InfoSci Platform
Reference Hub1
Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data

Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data

Angelin Gladston, Arjun Sharmaa I., Bagirathan S. S. K. G.
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 14
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781683181019|DOI: 10.4018/IJSSCI.312561
Cite Article Cite Article

MLA

Gladston, Angelin, et al. "Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data." IJSSCI vol.14, no.1 2022: pp.1-14. http://doi.org/10.4018/IJSSCI.312561

APA

Gladston, A., Sharmaa I., A., & Bagirathan S. S. K. G. (2022). Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-14. http://doi.org/10.4018/IJSSCI.312561

Chicago

Gladston, Angelin, Arjun Sharmaa I., and Bagirathan S. S. K. G. "Regression Approach for GDP Prediction Using Multiple Features From Macro-Economic Data," International Journal of Software Science and Computational Intelligence (IJSSCI) 14, no.1: 1-14. http://doi.org/10.4018/IJSSCI.312561

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Gross domestic product is the main measure used predominantly for assessing the wealth and growth of a country. Previous works used the amount of CO2 emitted by a country in predicting the gross domestic product growth of that quarter. Though it is a valid indicator, there are many other features that can be considered while calculating the gross domestic product of a country. In this paper, an approach to predict gross domestic product utilizing many features is introduced. Macroeconomic data like unemployment rate, gold rate, foreign exchange rate, and other important data to plot the graph are used for linear regression, employing dimensionality reduction to analyze and extract only the important features and thereby increasing the effectiveness of the proposed GDP prediction. Since data has been published in different time intervals, preprocessing like interpolation, reshaping, and dimensionality reduction using PCA are carried out to make the proposed GDP prediction model more precise and accurate, and the maximum accuracy of 95% is obtained.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.