This project focuses on Dimension Reduction using Principal Component Analysis (PCA). By analyzing a global air pollution dataset, I reduced five complex variables (AQI, PM2.5, Ozone, CO, NO2) into two principal components. This analysis helps identify the primary pollutants driving global air quality patterns and simplifies the data for better visualization and interpretation.