{ "data_id": "43364", "name": "Gambling-Behavior-Bustabit", "exact_name": "Gambling-Behavior-Bustabit", "version": 1, "version_label": "v1.0", "description": "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.\nAlong 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:\nYou 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.\nYour 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\n\nThe multiplier increases as time goes on, but if you wait too long to cash out, you may bust and lose your money.\nLastly, the house maintains slight advantages because in 1 out of every 100 games, everyone playing busts.", "format": "arff", "uploader": "Elif Ceren Gok", "uploader_id": 30125, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-23 12:40:00", "update_comment": null, "last_update": "2022-03-23 12:40:00", "licence": "GPL 2", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102189\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "Gambling-Behavior-Bustabit", "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 Bustab " ], "weight": 5 }, "qualities": { "NumberOfInstances": 50000, "NumberOfFeatures": 9, "NumberOfClasses": null, "NumberOfMissingValues": 63798, "NumberOfInstancesWithMissingValues": 21266, "NumberOfNumericFeatures": 7, "NumberOfSymbolicFeatures": 0, "Dimensionality": 0.00018, "PercentageOfNumericFeatures": 77.77777777777779, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 42.532, "AutoCorrelation": null, "PercentageOfMissingValues": 14.177333333333333 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" }, { "uploader": "38960", "tag": "Machine Learning" } ], "features": [ { "name": "Id", "index": "0", "type": "numeric", "distinct": "50000", "missing": "0", "min": "854", "max": "26978524", "mean": "13530504", "stdev": "7768258" }, { "name": "GameID", "index": "1", "type": "numeric", "distinct": "42152", "missing": "0", "min": "3294811", "max": "3436562", "mean": "3363443", "stdev": "41607" }, { "name": "Username", "index": "2", "type": "string", "distinct": "4149", "missing": "0" }, { "name": "Bet", "index": "3", "type": "numeric", "distinct": "2758", "missing": "0", "min": "1", "max": "1000000", "mean": "2935", "stdev": "30652" }, { "name": "CashedOut", "index": "4", "type": "numeric", "distinct": "478", "missing": "21266", "min": "1", "max": "126", "mean": "2", "stdev": "2" }, { "name": "Bonus", "index": "5", "type": "numeric", "distinct": "722", "missing": "21266", "min": "0", "max": "12", "mean": "1", "stdev": "2" }, { "name": "Profit", "index": "6", "type": "numeric", "distinct": "9057", "missing": "21266", "min": "0", "max": "1175993", "mean": "1535", "stdev": "20001" }, { "name": "BustedAt", "index": "7", "type": "numeric", "distinct": "3472", "missing": "0", "min": "0", "max": "251025", "mean": "17", "stdev": "1173" }, { "name": "PlayDate", "index": "8", "type": "string", "distinct": "42152", "missing": "0" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }