This Market Basket Analysis of the UK Online Retail dataset shows that consumer behavior is primarily driven by the completion of thematic collections and seasonal shifts. Using Apriori and ECLAT algorithms, the study identified powerful products alongside a tenfold increase in Christmas-themed transactions during November and December. High-support bundles were consistently found in utility categories where customers frequently purchase multiple color variants in a single transaction. To maximize revenue, the retailer should implement automated recommendations and pre-packaged bundles that encourage customers to complete these high-confidence product sets.

This project explores the application of unsupervised learning techniques to analyze a high-dimensional dataset of household energy consumption and environmental conditions. Using Principal Component Analysis (PCA) with Varimax rotation and Non-metric Multidimensional Scaling (MDS), the study reduces 25 sensor variables into five interpretable factors. The analysis focuses on identifying the primary drivers of energy usage and detecting anomalies. A Procrustes Analysis is further employed to validate the consistency between linear and non-linear dimensionality reduction approaches, providing a framework for energy usage data diagnostics.

This project investigates the seasonal patterns of Washington bike-sharing demand using clustering. By aggregating hourly rental data into daily profiles, the study applies three distinct clustering algorithms: K-Means, PAM, and Hierarchical Agglomerative Clustering (HAC), to segment the data based on environmental and calendar factors.