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Dimension Reduction: Principal Component Analysis (PCA) on Global Air Quality Data
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.
2025 July - December Netflix Engagement with the K-Wave
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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