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

Abstract

V tem diplomskem delu smo preučevali trende gibanja cen pri kartah igre Magic: The Gathering in pri tem uporabili najbolj primerne metode strojnega ućenja. Cilj je bil izdelati napovedni model za cene kart. Naša naloga je bila identiciranje pomembnih virov, pridobivanje potrebnih podatkov, njihova pretvorba v računalniku razumljivo obliko ter izbira primernega algoritma. Model, ki smo ga ustvarili, se je izkazal za zanesljivega s 61% točnostjo napovedi gibanja cene pri zelo redkih kartah, medtem ko smo pri redkih kartah dosegli le 52% točnost, kar ni preseglo niti privzete točnosti. Pri nalogi smo uporabili metodo podpornih vektorjev ter si pomagali z orodjem Weka. S podatki, ki smo jih pridobili, smo naredili še nekaj poizkusov in tako poiskali nekaj novih odvisnosti med podatki, ki jih prej nismo poznali.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