This project explores Market Basket Analysis (MBA) using the Apriori algorithm on a dataset of 9,835 grocery transactions. The study aims to uncover hidden relationships between products by calculating Support, Confidence, and Lift metrics. Key associations are visualized through interactive network graphs and scatter plots, providing actionable insights into consumer purchasing behavior and product placement strategies.

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.

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.