{
  "id": 329057,
  "title": "question from newbie",
  "url": "/competitions/amex-default-prediction/discussion/329057",
  "author_name": "",
  "post_date": "2022-06-04T14:04:52.368189300Z",
  "votes": 10,
  "comment_count": 4,
  "views": 0,
  "content": "<p>Hey , <br>\nI want to ask something , I know competition pretty serious( and obviously not my level ) and data set way too big but I cant read data even  with google cloud ( with free credit computers they gave 8cpu  32gb ram or something like that )</p>\n<p>Do all participants have acces to super computers or do I missing something here ?</p>\n<p>Thanks for all the help.</p>",
  "messages": [
    {
      "id": "1811268",
      "postDate": "06/04/2022 14:04:52",
      "content": "<p>Hey , <br>\nI want to ask something , I know competition pretty serious( and obviously not my level ) and data set way too big but I cant read data even  with google cloud ( with free credit computers they gave 8cpu  32gb ram or something like that )</p>\n<p>Do all participants have acces to super computers or do I missing something here ?</p>\n<p>Thanks for all the help.</p>",
      "rawMarkdown": "Hey , \nI want to ask something , I know competition pretty serious( and obviously not my level ) and data set way too big but I cant read data even  with google cloud ( with free credit computers they gave 8cpu  32gb ram or something like that )\n\nDo all participants have acces to super computers or do I missing something here ?\n\nThanks for all the help.",
      "votes": null
    },
    {
      "id": "1811287",
      "postDate": "06/04/2022 14:24:57",
      "content": "<p>No need for Supercomputers😄</p>\n<p>Here are few links you can look for reading large datasets : <br>\n<a href=\"https://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook\" target=\"_blank\">https://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook</a><br>\n<a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/327138\" target=\"_blank\">https://www.kaggle.com/competitions/amex-default-prediction/discussion/327138</a><br>\n<a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/327143\" target=\"_blank\">https://www.kaggle.com/competitions/amex-default-prediction/discussion/327143</a></p>\n<p>And For the Baseline models : <br>\n<a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/328846\" target=\"_blank\">https://www.kaggle.com/competitions/amex-default-prediction/discussion/328846</a><br>\n<a href=\"https://www.kaggle.com/code/ambrosm/amex-lightgbm-quickstart\" target=\"_blank\">https://www.kaggle.com/code/ambrosm/amex-lightgbm-quickstart</a><br>\n<a href=\"https://www.kaggle.com/code/huseyincot/amex-catboost-0-793\" target=\"_blank\">https://www.kaggle.com/code/huseyincot/amex-catboost-0-793</a></p>",
      "rawMarkdown": "No need for Supercomputers😄\n\nHere are few links you can look for reading large datasets : \nhttps://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook\nhttps://www.kaggle.com/competitions/amex-default-prediction/discussion/327138\nhttps://www.kaggle.com/competitions/amex-default-prediction/discussion/327143\n\nAnd For the Baseline models : \nhttps://www.kaggle.com/competitions/amex-default-prediction/discussion/328846\nhttps://www.kaggle.com/code/ambrosm/amex-lightgbm-quickstart\nhttps://www.kaggle.com/code/huseyincot/amex-catboost-0-793",
      "votes": null
    },
    {
      "id": "1811296",
      "postDate": "06/04/2022 14:37:27",
      "content": "<p>hahah I'll check those out. <br>\nThanks a lot.</p>",
      "rawMarkdown": "hahah I'll check those out. \nThanks a lot.",
      "votes": null
    },
    {
      "id": "1811540",
      "postDate": "06/04/2022 19:43:37",
      "content": "<p>I suggest using Raddar's dataset. He has reduced the data from 50GB to 5GB by removing noise. His discussion is <a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/328514\" target=\"_blank\">here</a> with links to his dataset within</p>",
      "rawMarkdown": "I suggest using Raddar's dataset. He has reduced the data from 50GB to 5GB by removing noise. His discussion is [here][1] with links to his dataset within\n\n[1]: https://www.kaggle.com/competitions/amex-default-prediction/discussion/328514",
      "votes": null
    },
    {
      "id": "1813055",
      "postDate": "06/06/2022 13:44:10",
      "content": "<p>That's incredibly helpful Chris. Thanks a lot.</p>",
      "rawMarkdown": "That's incredibly helpful Chris. Thanks a lot.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1811287,
      "author_name": "devkhant24",
      "author_url": "",
      "post_date": "06/04/2022 14:24:57",
      "content": "<p>No need for Supercomputers😄</p>\n<p>Here are few links you can look for reading large datasets : <br>\n<a href=\"https://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook\" target=\"_blank\">https://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook</a><br>\n<a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/327138\" target=\"_blank\">https://www.kaggle.com/competitions/amex-default-prediction/discussion/327138</a><br>\n<a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/327143\" target=\"_blank\">https://www.kaggle.com/competitions/amex-default-prediction/discussion/327143</a></p>\n<p>And For the Baseline models : <br>\n<a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/328846\" target=\"_blank\">https://www.kaggle.com/competitions/amex-default-prediction/discussion/328846</a><br>\n<a href=\"https://www.kaggle.com/code/ambrosm/amex-lightgbm-quickstart\" target=\"_blank\">https://www.kaggle.com/code/ambrosm/amex-lightgbm-quickstart</a><br>\n<a href=\"https://www.kaggle.com/code/huseyincot/amex-catboost-0-793\" target=\"_blank\">https://www.kaggle.com/code/huseyincot/amex-catboost-0-793</a></p>",
      "votes": null,
      "replies": [
        {
          "id": 1811296,
          "author_name": "hakandeveli",
          "author_url": "",
          "post_date": "06/04/2022 14:37:27",
          "content": "<p>hahah I'll check those out. <br>\nThanks a lot.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 1811540,
      "author_name": "cdeotte",
      "author_url": "",
      "post_date": "06/04/2022 19:43:37",
      "content": "<p>I suggest using Raddar's dataset. He has reduced the data from 50GB to 5GB by removing noise. His discussion is <a href=\"https://www.kaggle.com/competitions/amex-default-prediction/discussion/328514\" target=\"_blank\">here</a> with links to his dataset within</p>",
      "votes": null,
      "replies": [
        {
          "id": 1813055,
          "author_name": "hakandeveli",
          "author_url": "",
          "post_date": "06/06/2022 13:44:10",
          "content": "<p>That's incredibly helpful Chris. Thanks a lot.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1811268": "Hey , \nI want to ask something , I know competition pretty serious( and obviously not my level ) and data set way too big but I cant read data even  with google cloud ( with free credit computers they gave 8cpu  32gb ram or something like that )\n\nDo all participants have acces to super computers or do I missing something here ?\n\nThanks for all the help.",
    "1811287": "No need for Supercomputers😄\n\nHere are few links you can look for reading large datasets : \nhttps://www.kaggle.com/code/rohanrao/tutorial-on-reading-large-datasets/notebook\nhttps://www.kaggle.com/competitions/amex-default-prediction/discussion/327138\nhttps://www.kaggle.com/competitions/amex-default-prediction/discussion/327143\n\nAnd For the Baseline models : \nhttps://www.kaggle.com/competitions/amex-default-prediction/discussion/328846\nhttps://www.kaggle.com/code/ambrosm/amex-lightgbm-quickstart\nhttps://www.kaggle.com/code/huseyincot/amex-catboost-0-793",
    "1811296": "hahah I'll check those out. \nThanks a lot.",
    "1811540": "I suggest using Raddar's dataset. He has reduced the data from 50GB to 5GB by removing noise. His discussion is [here][1] with links to his dataset within\n\n[1]: https://www.kaggle.com/competitions/amex-default-prediction/discussion/328514",
    "1813055": "That's incredibly helpful Chris. Thanks a lot."
  },
  "source": "meta"
}