Predicting gross domestic product using the ensemble machine learning method

Abstract

The need for more accurate GDP predictions in Nigeria has necessitated the exploration of additional indicators that reflect economic activities and socio-economic factors. This research pioneers a comprehensive approach to predicting Nigeria's Gross Domestic Product (GDP) by integrating a wide array of indicators beyond traditional economic metrics. The primary objective is to enhance the prediction accuracy of Nigeria's GDP using a diverse range of socio-economic indicators. Drawing from data spanning 2000 to 2021, the study incorporates variables like healthcare expenditure, net migration rates, population demographics, life expectancy, access to electricity, and internet usage. Utilising machine learning techniques such as Random Forest Regressor, XGBoost Regressor, and Linear Regression, the study rigorously evaluates the efficacy of these algorithms in forecasting GDP. The analysis reveals that all selected indicators have a strong correlation with GDP. Significantly, the Random Forest Regressor emerges as the most robust model, boasting an R2 score of 0.96 and a Mean Absolute Error (MAE) of 24.29. The study underscores that optimising factors like healthcare, internet access, and electricity availability could serve as pivotal levers for accelerating Nigeria's economic growth.

Keywords

• GDP
• Electricity access
• Healthcare Spending
• Life Expectancy
• Machine Learning
• Random Forest Regressor