ABSTRACT
The outbreak of the COVID-19 pneumonia in 2019 has caused great damage to the world economy. With the continuous growth of the amount of data, using machine learning algorithm to analyze and predict the economic development of different countries and regions is a hot topic in recent years. In this paper, three machine learning algorithms (XGBoost, AdaBoost and random forest algorithms) are coupled together, and a new algorithm is proposed. Combined with data preprocessing and fine feature engineering processing, GDP values of different countries and regions are predicted. Experimental results show that our coupled method has better performance than each single machine learning algorithm used in this paper. Specifically, the MSE metrics of proposed model is 1.64%, 3.69% and 8.95% lower than XGBoost, AdaBoost and Random Forest algorithm, respectively. In addition, we also study the correlation coefficient between features and get some constructive guidance to improve the accuracy of the algorithm and restrain the further development of the epidemic situation. © 2021 IEEE.