The key to every successful business is the understanding of it’s customers. In order to increase sales and profit companies has to discover what do their clients like, how much money are they able to spend on offered products and which product categories are they willing to buy together in a single transaction. Association rules are very helpful when it comes to mining for patterns in customers behavior. The aim of this paper is to mine for association rules in dataset with transactions from bakery and to check whether those rules found in morning transactions differ from afternoon ones.

The aim of this project is to use PCA (Principal Component Analysis) algorithm for dimension reduction on the McDonald's menu dataset. Problem of high dimensional data occurs when the dimension of the dataset (each numeric variable is a dimension) is large in comparison to number of observations. The goal of dimension reduction is to decrease the size of the dataset preserving as much information as possible.

The aim of this paper is to examine whether dividing all European national teams into four groups was optimal or another number of groups would be more proper. Additional aim is to check which teams should be allocated together depending on individual players profiles. The research was conducted using various clustering methods.