It is becoming increasingly popular for training staffs in football to use statistics as indicators. For a long time, the predominant method of analysis was the so-called eye test, which involved simply looking at players. But over time, and with the development of technology, detailed statistics began to be used to analyse individual players. Goal of this project is to cluster football players into groups(playstyles), based on their statistics. Dataset used in this analysis are statistics from 18/19 English Premier league season

In the realm of big data, datasets more often than not are enormous and include many variables, making pattern analysis and visualization a challenging task. High-dimensional datasets could lead to redundancy, noisy traits, and inefficient computations. Dimension reduction techniques help reduce the volume of information while preserving the maximum amount of relevant information.

Market Basket Analysis is a fundamental technique in data mining used to uncover relationships between items frequently purchased together. This project applies Association Rule Mining to the built-in ‘Groceries’ dataset in R, utilizing the Apriori algorithm to identify meaningful associations between grocery items. The results of this analysis can help businesses optimize inventory management, improve product placements, and enhance cross-selling strategies