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.

A brief report describing and visualising the prevalence of Korean titles in Netflix's sixth engagement report focusing on the hours viewed metric.

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This project uses Unsupervised Learning (CLARA algorithm) to analyze air quality data from cities worldwide. It identifies distinct pollution profiles based on AQI, PM2.5, Ozone, CO, and NO2 values to group cities into different environmental categories.