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Gambling-Behavior-Bustabit

Gambling-Behavior-Bustabit

active ARFF GPL 2 Visibility: public Uploaded 23-03-2022 by Elif Ceren Gok
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  • Computer Systems Machine Learning
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The similarities and differences in the behaviors of different people have long been of interest, particularly in psychology and other social science fields. Understanding human behavior in particular contexts can help us to make informed decisions. Consider a game of poker - understanding why players raise, call, and fold in various situations can provide a distinct advantage competitively. Along these lines, we are going to focus on the behavior on online gamblers from a platform called Bustabit. There are a few basic rules for playing a game of Bustabit: You bet a certain amount of money (in Bits, which is 1 / 1,000,000th of a Bitcoin) and you win if you cash out before the game busts. Your win is calculated by the multiplier value at the moment you cashed out. For example, if you bet 100 and the value was 2.50x at the time you cashed out, you win 250. In addition, a percentage Bonus per game is multiplied with your bet and summed to give your final Profit in a winning game. Assuming a Bonus of 1, your Profit for this round would be (100 x 2.5) + (100 x .01) - 100 = 151 The multiplier increases as time goes on, but if you wait too long to cash out, you may bust and lose your money. Lastly, the house maintains slight advantages because in 1 out of every 100 games, everyone playing busts.

9 features

Idnumeric50000 unique values
0 missing
GameIDnumeric42152 unique values
0 missing
Usernamestring4149 unique values
0 missing
Betnumeric2758 unique values
0 missing
CashedOutnumeric478 unique values
21266 missing
Bonusnumeric722 unique values
21266 missing
Profitnumeric9057 unique values
21266 missing
BustedAtnumeric3472 unique values
0 missing
PlayDatestring42152 unique values
0 missing

19 properties

50000
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
63798
Number of missing values in the dataset.
21266
Number of instances with at least one value missing.
7
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
77.78
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
42.53
Percentage of instances having missing values.
Average class difference between consecutive instances.
14.18
Percentage of missing values.

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