The Titanic disaster remains one of the most analyzed historical events due to its tragic loss of life and the notable patterns among survivors (and probably very popular and available dataset :D). Using association rules, this project aims to uncover hidden relationships between passenger attributes and survival outcomes leveraging Apriori algorithm to find meaningful associations in the Titanic dataset, revealing patterns in demographics, ticket class, family structure, and embarkation points.This project is conducted using the Titanic dataset from Kaggle (https://www.kaggle.com/c/titanic/data?select=train.csv), transformed into a transaction-based format for association rule mining.

Coffee is inseparable part of exam session season and for may people, inseparable part of every-day life. This study is based on dataset from Kaggle [https://www.kaggle.com/datasets/volpatto/coffee-quality-database-from-cqi/data] that gathers 1339 observations from Coffee Quality Institute about multiple features of different coffee beans. The analysis aims to: a) perform dimension reduction on coffee quality features using Principal Component Analysis, b) investigate if different types of coffee described in the dataset differ significantly or are rather uniform. c) see if results from dimension reduction using PCA and clustering have something in common and drive conclusions form it.