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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 winning team based on hero matchup given a dataset of 102,944 individual matchups and their labeled outcomes. We'll employ the [Naive Bayes](https://rubixml.github.io/ML//latest/classifiers/naive-bayes.html) 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 to new data.\n\n- **Difficulty:** Easy\n- **Training time:** Minutes\n\n## Installation\nClone the project locally using [Composer](https://getcomposer.org/):\n```sh\n$ composer create-project rubix/dota2\n```\n\n## Requirements\n- [PHP](https://php.net) 7.2 or above\n\n#### Recommended\n- 2G of system memory or more\n\n## Tutorial\n\nOn the map ...\n\n## Original Dataset\nstephen.tridgell '@' sydney.edu.au\n\n## References\n\u003e- 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.\n\n## License\nThe code is licensed [MIT](LICENSE) and the tutorial is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubixml%2Fdota2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frubixml%2Fdota2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frubixml%2Fdota2/lists"}