FORECASTING GROSS DOMESTIC PRODUCT (GDP) AND GDP GROWTH: AN EXPLORATION OF IMPROVED PREDICTION USING MACHINE LEARNING ALGORITHMS

Azibaev Akhmadkhon Gulomjon ugli

doi:10.54613/ku.v1i1.334

FORECASTING GROSS DOMESTIC PRODUCT (GDP) AND GDP GROWTH: AN EXPLORATION OF IMPROVED PREDICTION USING MACHINE LEARNING ALGORITHMS

Authors

Azibaev Akhmadkhon Gulomjon ugli PhD student of Namangan State University (Uzbekistan)

DOI:

https://doi.org/10.54613/ku.v1i1.334

Keywords:

Gross Domestic Product (GDP), GDP growth, machine learning algorithms, random forest regression, linear regression, autoregressive integrated moving average (ARIMA), forecasting, economic analysis, decision-making.

Abstract

This article explores the significance of Gross Domestic Product (GDP) and GDP growth, the importance of accurate forecasting, and the role of machine learning algorithms in improving prediction accuracy. It reviews several studies that highlight the effectiveness of machine learning algorithms, such as random forest regression, linear regression, and autoregressive integrated moving average (ARIMA), in GDP forecasting. These algorithms analyze data, identify patterns, and make accurate forecasts, contributing to enhanced decision-making in economic analysis and planning.

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FORECASTING GROSS DOMESTIC PRODUCT (GDP) AND GDP GROWTH: AN EXPLORATION OF IMPROVED PREDICTION USING MACHINE LEARNING ALGORITHMS

FORECASTING GROSS DOMESTIC PRODUCT (GDP) AND GDP GROWTH: AN EXPLORATION OF IMPROVED PREDICTION USING MACHINE LEARNING ALGORITHMS

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