https://github.com/sfirke/predicting-march-madness
Machine learning tutorial to create an entry for the Kaggle March Mania contest
https://github.com/sfirke/predicting-march-madness
introduction machine-learning march-madness
Last synced: about 1 month ago
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Machine learning tutorial to create an entry for the Kaggle March Mania contest
- Host: GitHub
- URL: https://github.com/sfirke/predicting-march-madness
- Owner: sfirke
- License: mit
- Created: 2016-03-05T02:46:16.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2019-03-21T17:34:24.000Z (over 6 years ago)
- Last Synced: 2024-11-15T04:47:22.681Z (11 months ago)
- Topics: introduction, machine-learning, march-madness
- Language: R
- Homepage:
- Size: 16.1 MB
- Stars: 30
- Watchers: 6
- Forks: 12
- Open Issues: 5
-
Metadata Files:
- Readme: README.Rmd
- License: License.md
Awesome Lists containing this project
README
---
output:
md_document:
variant: markdown_github
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
options(width = 110)
```
## Predicting March Madness
Kaggle's [March Madness prediction competition](https://www.kaggle.com/c/mens-machine-learning-competition-2018/) is an accessible introduction to machine learning. If you happen to like college basketball, you'll like that in this competition you can't bust your bracket, since you make a prediction for every game. Plus this year there's a big prize pool, and luck plays a big enough role that you can be a legit contender fairly easily.
In 2016, my simple process using tidyverse functions in R placed in the top 10%. I refined it a bit for 2017 and finished in the top 25%.
I'm sharing my code and process here for others to use as a starting point. My approach is similar to that of the 2014 winners, Gregory Matthews and Michael Lopez. They published [a paper about the role that luck plays in this competition](https://arxiv.org/abs/1412.0248), putting their model in perspective. A takeaway: take my model, tweak it a bit to generate some distance from the field, and you are competitive to win!
## What's here
In the Kaggle competition, you estimate how likely it is that Team A beats Team B, for each of the 2,278 possible matchups in the tournament. **[My guide](march_madness_how_to.md)** documents a set of scripts for each step of:
* Deciding on possible input parameters
* Scraping the input data with the `rvest` package
* Cleaning and joining data sources to get tidy, prediction-ready data
* Training and evaluating machine learning models on the data
* Making and submitting predictions
## Licensing/usage
This code is public, please reuse it. It's under an [MIT license](License.md). Please acknowledge its role in any write-up or discussion of work that relies on it. And if you win a cash prize from Kaggle using this, congratulations! I wouldn't turn down a thank-you gift ;)
## Thanks
Thanks to contributors **@MHenderson** and **@BillPetti**.
## Contact me
Let me know what you think, either on twitter @samfirke or compose a friendly e-mail to: 