{
  "id": 134957,
  "title": "Julia XGBoost Solution",
  "url": "/competitions/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/discussion/134957",
  "author_name": "stefan",
  "post_date": "2020-03-11T10:36:40.389000",
  "votes": 2,
  "comment_count": 0,
  "views": 0,
  "content": "<p>I've been an R user turned Python user, but I have lately been obsessed with the speed and elegance of Julia. I have been working on a model using Julia as a learning exercise, and given the low penetration of the language I am happy to share my code for other Julia newbies. </p>\n\n<p>The repo below is a Julia package to generate a number of common features used in this competition, and runs an ensemble of XGBoost models to predict the outcome. Feel free to clone and improve!</p>\n\n<p>You can install it by simply running:\n<code>Pkg.add(\"https://github.com/stefanjwojcik/mm2020\")</code></p>\n\n<p><a href=\"https://github.com/stefanjwojcik/mm2020\">https://github.com/stefanjwojcik/mm2020</a></p>",
  "messages": [
    {
      "id": 768908,
      "postDate": "2020-03-11T10:36:40.390Z",
      "content": "<p>I've been an R user turned Python user, but I have lately been obsessed with the speed and elegance of Julia. I have been working on a model using Julia as a learning exercise, and given the low penetration of the language I am happy to share my code for other Julia newbies. </p>\n\n<p>The repo below is a Julia package to generate a number of common features used in this competition, and runs an ensemble of XGBoost models to predict the outcome. Feel free to clone and improve!</p>\n\n<p>You can install it by simply running:\n<code>Pkg.add(\"https://github.com/stefanjwojcik/mm2020\")</code></p>\n\n<p><a href=\"https://github.com/stefanjwojcik/mm2020\">https://github.com/stefanjwojcik/mm2020</a></p>",
      "rawMarkdown": "I've been an R user turned Python user, but I have lately been obsessed with the speed and elegance of Julia. I have been working on a model using Julia as a learning exercise, and given the low penetration of the language I am happy to share my code for other Julia newbies. \n\nThe repo below is a Julia package to generate a number of common features used in this competition, and runs an ensemble of XGBoost models to predict the outcome. Feel free to clone and improve!\n\nYou can install it by simply running:\n`Pkg.add(\"https://github.com/stefanjwojcik/mm2020\")`\n\nhttps://github.com/stefanjwojcik/mm2020",
      "votes": 2
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "768908": "I've been an R user turned Python user, but I have lately been obsessed with the speed and elegance of Julia. I have been working on a model using Julia as a learning exercise, and given the low penetration of the language I am happy to share my code for other Julia newbies. \n\nThe repo below is a Julia package to generate a number of common features used in this competition, and runs an ensemble of XGBoost models to predict the outcome. Feel free to clone and improve!\n\nYou can install it by simply running:\n`Pkg.add(\"https://github.com/stefanjwojcik/mm2020\")`\n\nhttps://github.com/stefanjwojcik/mm2020"
  }
}