This task involves clustering Uber trip data by location, analyzing cluster centers over time and date.

This project applies PCA and t-SNE to World Bank Development Indicators (2021) to reveal global economic patterns and local country clusters.

This homework is aimed to search the patterns across European Social Survey, especially focusing on “Feeling about household’s income nowadays” variable as a rule consequent.

This research evaluates the effectiveness and consistency of three clustering evaluation metrics by applying them to images with distinct visual and structural properties—a complex mural, a photo of pierogi, and a Baltic Sea view—and analyzing both metric results and visual segmentations to identify performance patterns and conditions favoring specific methods.

This project applies Principal Component Analysis (PCA) to the coffee quality dataset as an effective dimensionality reduction technique for analyzing high-dimensional data, preserving key information, and enabling visualization beyond two dimensions.

This project applies association rule mining to analyze relationships between symptoms and diseases, treating symptoms as both antecedents and consequents, while also addressing specific personal symptom-related questions.