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Unsupervised Learning Applied to the Segmentation of Users of Online Gambling Platforms in Portugal - The effects of the Covid-19 Pandemic on User Behavior and Segmentation
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Orientador(es)
Resumo(s)
Online gambling has become an increasingly relevant activity in the last years and is now available
through a wide variety of technologies and platforms. This can be seen as an important addition to the
entertainment industry since it has the potential of generating great economic impacts. The
phenomenon, however, is not free of concerns considering that, like in any other type of gambling
activities, online gamblers are susceptible to developing behavioral addiction. This has become a
reason of concern to many governmental bodies around the world which are studying this issue due
to its social impacts on the population. In this context machine learning algorithms can be applied to
understand the behavior of online gamblers and to identify the characteristics of gambling addiction.
This work project has the objective of segmentizing users of online gambling platforms in Portugal
according to the tendency of these users to have compulsive gambling behavior. It also intends to
evaluate the impacts of the Covid-19 pandemic on online gambling addiction by analyzing changes in
user segmentation during the initial periods of the pandemic. This will be done by applying
unsupervised learning algorithms, specifically K-Means and Self-Organizing Maps and by comparing
user clusters from the years 2019 and 2020.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
Palavras-chave
Artificial Intelligence Big Data Clustering Data Science Machine Learning Segmentation Unsupervised Learning