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Clustering Bustabit Gambling Behavior

Use cluster analysis to glean insights into cryptocurrency gambling behavior.

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  • 9 tasks
  • 261 participants
  • 1,500 XP

Project Description

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, the 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.

Project Tasks

  • 1A preliminary look at the Bustabit data
  • 2Deriving relevant features for clustering
  • 3Creating per-player statistics
  • 4Scaling and normalization of the derived features
  • 5Cluster the player data using K means
  • 6Compute averages for each cluster
  • 7Visualize the clusters with a Parallel Coordinate Plot
  • 8Visualize the clusters with Principal Components
  • 9Analyzing the groups of gamblers our solution uncovered
Instructor Avatar
Eric Hare

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.

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Instructor Avatar
Lawrence Mosley

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.

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Technology

  • R LogoR
  • Topics

    Data ManipulationData VisualizationMachine LearningImporting & Cleaning Data