This study explores economic patterns among advanced economies using unsupervised learning techniques. The analysis is particularly relevant in the context of ongoing debates about economic convergence and divergence among developed nations, especially following the global financial crisis and the COVID-19 pandemic.

The aim of this study is to use association rules to identify patterns and dependencies related to Himalayan expeditions. The data comes from the Himalayan Database [https://www.himalayandatabase.com/] and includes expeditions from 1990 to 2024. The starting point of this analysis, 1990, marks the beginning of the commercial era of Himalayan climbing. The analysis focuses on expedition-level data rather than individual climbers, as this approach provides a better understanding of both the risks and the overall safety of the expeditions. In this study, an expedition is considered successful only when all its members return safely. The Apriori algorithm was used to perform the analysis, with each row in the dataset representing a unique expedition.

Unsupervised Learning project #3

This project explores dimensionality reduction and clustering techniques using the Fashion MNIST dataset, a collection of grayscale images representing 10 different categories of clothing items. The goal is to understand how to reduce high-dimensional data while preserving important structures and patterns, and then cluster similar images together.

An introduction to using RMarkdown making use of data sets arbuthnot and present.

Comp 3182 labs

Differential Gene Expression (DGE) analysis identifies genes with significant expression changes across conditions using RNA-Seq data. This process involves normalization, statistical modeling, and significance testing. Three common R packages—DESeq2, edgeR, and limma-voom—use different approaches to detect differentially expressed genes (DEGs). DESeq2 applies a negative binomial model, edgeR utilizes the TMM method and GLM, and limma-voom transforms count data to log2 scale for linear modeling. Combining DEGs from multiple methods enhances robustness. Further analysis, such as Gene Ontology (GO) and KEGG pathway enrichment, helps interpret biological significance, ensuring reliable transcriptomic insights.