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.

The primary objective of this study is to perform dimensionality reduction using the Principal Component Analysis (PCA) method. The analysis is conducted on a dataset sourced from Kaggle [https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009], focusing on the quality assessment of red wine. The dataset originates from the publication: P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis. “Modeling wine preferences by data mining from physicochemical properties.” Decision Support Systems, Elsevier, 47(4):547–553, 2009. The dataset comprises 1,599 observations and 12 numerical variables, with input features derived from physicochemical tests and an output variable reflecting sensory evaluations of wine quality.

This project aims to investigate whether ski resorts exhibit significant differences or if their characteristics are largely uniform, irrespective of location or features. The analysis is based on a dataset sourced from Kaggle [https://www.kaggle.com/datasets/farheenshaukat/ski-resort], which provides detailed information about ski resorts, including their geographical location, pricing, slope characteristics, lift infrastructure, and snow cannon availability.