This analysis explores the clustering behavior of clubgoers based on various preferences, entry times, and demographic information. Using unsupervised learning techniques, we compare the performance of K-Means and DBSCAN clustering algorithms. The optimal number of clusters is determined using the elbow method, and the clustering results are visualized using PCA (Principal Component Analysis) to reveal distinct patterns within the data. DBSCAN's ability to detect irregular clusters and noise is compared with K-Means' performance on well-separated clusters. This study highlights the strengths and limitations of both algorithms in identifying meaningful segments among clubgoers.

This is a transactional data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.

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PPV1 isolates from wild boars were collected in 2022-2024 hunting seasons in Moscow, Tver and Belgorod regions. After phylogenetic analysis of VP1/2 sequences, 27-a-like strains were marked by red colour and Kresse-like strains were marked by yellow colour.