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Predicting the prices of cards in the game Magic with machine learning

Jernej Lipovec (2016) Predicting the prices of cards in the game Magic with machine learning. EngD thesis.

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    This thesis is a study of Magic: The Gathering card price fluctuations using the most appropriate machine learning methods. The goal was to construct a predictive model for card prices. This required us to identify crucial attributes, gather necessary data, convert it to a machine-readable format and select a suitable learning algorithm for the task. The resulting model was effective, attaining a 61 % price trend accuracy with mythic rare cards, while it was less successful with rare cards with only 52% accuracy, which failed to beat default accuracy. Support vector machines algorithms and the machine learning toolbox Weka were used to achieve these results, which were applied in further experiments that led to the discovery of previously unknown data dependencies.

    Item Type: Thesis (EngD thesis)
    Keywords: machine learning, price prediction, MTG, free market, supply and demand, support vector machines, Weka, data mining
    Number of Pages: 42
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    akad. prof. dr. Ivan Bratko77Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536872387)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3299
    Date Deposited: 23 Mar 2016 11:47
    Last Modified: 18 Apr 2016 13:01
    URI: http://eprints.fri.uni-lj.si/id/eprint/3299

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