Modeling and Forecasting Global GDP Trends Using PCA and Machine Learning: Evidence from 1960 to 2020

This paper analyzes the GDP (PPP) data of more than 200 countries from 1960 to 2020, and explores the economic growth trend and its influencing factors. This study reveals the structural patterns behind GDP data and predicts future economic trends through methods such as data cleaning, PCA reduction and machine learning. The results show that the linear regression model has the best prediction effect on GDP, and its R² is close to 1, indicating that it effectively captures the main trends in the data. The article also analyzed the impact of the COVID-19 pandemic on China's GDP, pointing out that China's GDP grew by 8.1% in 2021, with GDP (purchasing power parity) increasing by 8.1%, while in 2022, GDP growth slowed down to 3.0%, with GDP (purchasing power parity) increasing by 4.9%. The research emphasizes the significance of GDP forecasting for policy-making and economic analysis, pointing out that GDP forecasting can assist the government in optimizing resource allocation, formulating scientific and reasonable economic policies, and providing references for enterprise risk hedging, supply chain optimization, and market access strategies.