{
  "id": 96785,
  "title": "Starter kit in R",
  "url": "/competitions/champs-scalar-coupling/discussion/96785",
  "author_name": "",
  "post_date": "2019-06-23T03:56:00.508749100Z",
  "votes": 17,
  "comment_count": 4,
  "views": 0,
  "content": "<p>I have shared some kernels and codes as building blocks for getting started in R.</p>\n\n<p>Kernel of a basic dashboard is <a href=\"https://www.kaggle.com/rohanrao/basic-dashboard-of-visualizations-in-r\">here</a>\nKernel of EDA, features, models (LGBM, XGBoost) benchmark is <a href=\"https://www.kaggle.com/rohanrao/eda-features-models-starter-code-in-r\">here</a>\nKernel for competition metric is <a href=\"https://www.kaggle.com/rohanrao/competition-metric-in-r\">here</a></p>\n\n<p>There are a lot more features to be explored (Andrew's <a href=\"https://www.kaggle.com/artgor/brute-force-feature-engineering\">FE kernel</a> is a great place to start) and different models and parameters to be tried which can improve your score.</p>",
  "messages": [
    {
      "id": "558863",
      "postDate": "06/23/2019 03:56:00",
      "content": "<p>I have shared some kernels and codes as building blocks for getting started in R.</p>\n\n<p>Kernel of a basic dashboard is <a href=\"https://www.kaggle.com/rohanrao/basic-dashboard-of-visualizations-in-r\">here</a>\nKernel of EDA, features, models (LGBM, XGBoost) benchmark is <a href=\"https://www.kaggle.com/rohanrao/eda-features-models-starter-code-in-r\">here</a>\nKernel for competition metric is <a href=\"https://www.kaggle.com/rohanrao/competition-metric-in-r\">here</a></p>\n\n<p>There are a lot more features to be explored (Andrew's <a href=\"https://www.kaggle.com/artgor/brute-force-feature-engineering\">FE kernel</a> is a great place to start) and different models and parameters to be tried which can improve your score.</p>",
      "rawMarkdown": "I have shared some kernels and codes as building blocks for getting started in R.\n\nKernel of a basic dashboard is [here](https://www.kaggle.com/rohanrao/basic-dashboard-of-visualizations-in-r)\nKernel of EDA, features, models (LGBM, XGBoost) benchmark is [here](https://www.kaggle.com/rohanrao/eda-features-models-starter-code-in-r)\nKernel for competition metric is [here](https://www.kaggle.com/rohanrao/competition-metric-in-r)\n\nThere are a lot more features to be explored (Andrew's [FE kernel](https://www.kaggle.com/artgor/brute-force-feature-engineering) is a great place to start) and different models and parameters to be tried which can improve your score.",
      "votes": null
    },
    {
      "id": "558894",
      "postDate": "06/23/2019 06:13:34",
      "content": "<p>Thanks <a href=\"/rohanrao\">@rohanrao</a>, these are really good R kernels. I may finally start using R... </p>",
      "rawMarkdown": "Thanks @rohanrao, these are really good R kernels. I may finally start using R...",
      "votes": null
    },
    {
      "id": "559544",
      "postDate": "06/24/2019 08:57:54",
      "content": "<p>Thank you very much <a href=\"/rohanrao\">@rohanrao</a> . It´s a pleasure to see some good R code in the Kernel´s section.</p>",
      "rawMarkdown": "Thank you very much @rohanrao . It´s a pleasure to see some good R code in the Kernel´s section.",
      "votes": null
    },
    {
      "id": "561975",
      "postDate": "06/26/2019 22:40:50",
      "content": "<p>Added basic LightGBM benchmark to the kernel. Runs in less than an hour.</p>",
      "rawMarkdown": "Added basic LightGBM benchmark to the kernel. Runs in less than an hour.",
      "votes": null
    },
    {
      "id": "562095",
      "postDate": "06/27/2019 01:26:03",
      "content": "<p>Always great to see R kernels. Thanks for sharing</p>",
      "rawMarkdown": "Always great to see R kernels. Thanks for sharing",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 558894,
      "author_name": "ogrellier",
      "author_url": "",
      "post_date": "06/23/2019 06:13:34",
      "content": "<p>Thanks <a href=\"/rohanrao\">@rohanrao</a>, these are really good R kernels. I may finally start using R... </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 559544,
      "author_name": "mddiaz",
      "author_url": "",
      "post_date": "06/24/2019 08:57:54",
      "content": "<p>Thank you very much <a href=\"/rohanrao\">@rohanrao</a> . It´s a pleasure to see some good R code in the Kernel´s section.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 561975,
      "author_name": "rohanrao",
      "author_url": "",
      "post_date": "06/26/2019 22:40:50",
      "content": "<p>Added basic LightGBM benchmark to the kernel. Runs in less than an hour.</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 562095,
      "author_name": "adrianoavelar",
      "author_url": "",
      "post_date": "06/27/2019 01:26:03",
      "content": "<p>Always great to see R kernels. Thanks for sharing</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "558863": "I have shared some kernels and codes as building blocks for getting started in R.\n\nKernel of a basic dashboard is [here](https://www.kaggle.com/rohanrao/basic-dashboard-of-visualizations-in-r)\nKernel of EDA, features, models (LGBM, XGBoost) benchmark is [here](https://www.kaggle.com/rohanrao/eda-features-models-starter-code-in-r)\nKernel for competition metric is [here](https://www.kaggle.com/rohanrao/competition-metric-in-r)\n\nThere are a lot more features to be explored (Andrew's [FE kernel](https://www.kaggle.com/artgor/brute-force-feature-engineering) is a great place to start) and different models and parameters to be tried which can improve your score.",
    "558894": "Thanks @rohanrao, these are really good R kernels. I may finally start using R...",
    "559544": "Thank you very much @rohanrao . It´s a pleasure to see some good R code in the Kernel´s section.",
    "561975": "Added basic LightGBM benchmark to the kernel. Runs in less than an hour.",
    "562095": "Always great to see R kernels. Thanks for sharing"
  },
  "source": "meta"
}