Abstract

For this project, I have compared the effectiveness of various feature sets from League of Legends (LoL) game data in classification tasks. Data for individual LoL matches were scraped from the North American match history servers. A decision tree, k-nearest-neighbors model, and multi-layer perceptron neural network were each configured and tested on their ability to identify player ranks when presented with game data. I used 13 different feature sets with each classifier and compared the results. The neural network always outperformed the other two models and the best feature set was the creep score intervals, gold earned intervals, and vision ward placement/destruction. The worst feature set was the losing team’s KDA by itself.

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