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Build a classifier to predict the outcome of Dota 2 games with the Naive Bayes algorithm and results from 102,944 sample games.
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README.md

Dota 2 Game Outcome Predictor

Dota 2 is a popular multiplayler online battle arena (MOBA) game that puts 10 players divided into 2 teams against each other. Each player controls a unique hero with abilities and its own set of strengths and weaknesses. Our objective is to build a classifier to predict the winner of a game based on the hero matchup given a dataset containing 102,944 individual matchups and their outcomes. We'll employ the Naive Bayes algorithm as our base estimator and learn how to save the trained model for use in another process. We'll also test the model to see how well it can generalize what it has learned.

  • Difficulty: Easy
  • Training time: Minutes
  • Memory needed: 2G

Installation

Clone the repository locally using Git:

$ git clone https://github.com/RubixML/Dota2

Install dependencies using Composer:

$ composer install

Requirements

  • PHP 7.1.3 or above

Tutorial

On the map ...

Original Dataset

stephen.tridgell '@' sydney.edu.au

References

  • Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
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