This project explores the relationship between international trade (imports and exports) and economic growth using the Global Economy Indicators dataset, which includes data from 200 countries spanning 1970 to 2021. By examining variables such as population, trade metrics, Gross Domestic Product (GDP), and per capita Gross National Income (GNI), the analysis identifies patterns, trends, and correlations that define the global economic landscape.

The project leverages both statistical and machine learning techniques to provide actionable insights:

• K-Means Clustering: An unsupervised machine learning algorithm groups countries into clusters based on their economic features, with scatter plots used to visualize these clusters in two dimensions.
• Linear Regression: A supervised learning algorithm predicts GDP based on other economic variables, highlighting linear relationships and assessing the influence of various factors on national economic performance.

This project delivers valuable insights into the interconnectedness of global trade and economic growth, offering practical knowledge for policymakers, investors, and the general public to better understand the drivers of economic performance and global interdependence.