Have you ever wondered if you could quantify the behavior of gamblers at the casino? Some seem to win the most, some can be reckless and risky with their bets, and others are casual about the whole experience. While collecting this data from the casino might be a challenge, there is an online platform called Bustabit in which gamblers can bet Bitcoin. We've collected data on thousands of Bustabit gambling sessions and tracked the user, the amount bet, the amount won, and various properties of the particular game itself. Using this data, you will perform a cluster analysis from start to finish in an attempt to group gamblers based on their gambling behavior.
To complete this project, students should be comfortable with R programming,
tidyverse package in particular, as the data manipulation and summarization
routines will use this. Introduction to the Tidyverse and
Introduction to Function Writing in R
are suggested as prerequisites. Basic knowledge and understanding of the concepts
of clustering (see Chapter 3 of Cluster Analysis in R)
is a plus but is not required.
The dataset used includes 10,000 games of Bustabit. Each game tracks the particular gambler, the BustedAt value of the game, and the multiplier at which the gambler cashed out.
Chief Data Scientist at Omni Analytics Group
Eric Hare is the Chief Data Scientist at Omni Analytics Group, a boutique statistical consulting firm specializing in data visualization, modeling, and Shiny applications. Eric graduated from Iowa State University with a PhD in Statistics and Computer Science in 2017 under the supervision of Dr. Heike Hofmann.See More
Founder of Omni Analytics Group
Lawrence Mosley is the Founder of Omni Analytics Group, a statistical consulting company specializing in machine learning, data strategy, Shiny development, and analytics training. He earned his PhD in Industrial Engineering at Iowa State University. Check him out on Twitter at @OmniAnalytics.See More