{
  "id": 328303,
  "title": "The large dataset must not make you give up reading it !",
  "url": "/competitions/amex-default-prediction/discussion/328303",
  "author_name": "",
  "post_date": "2022-05-31T19:33:27.216730300Z",
  "votes": 5,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Hello everyone !</p>\n<p>Following my notebook (attached to the post), you'll be able to read train data (~16GB) in much less memory (~2GB).</p>\n<p>You'll find also NaN values manipulation and the beginning of an EDA using Plotly !</p>\n<p>The notebook will be updated within future commits.</p>\n<p>Enjoy !</p>\n<p><a href=\"https://www.kaggle.com/code/tawejssh/american-express-default-prediction\" target=\"_blank\">https://www.kaggle.com/code/tawejssh/american-express-default-prediction</a></p>",
  "messages": [
    {
      "id": "1807175",
      "postDate": "05/31/2022 19:33:27",
      "content": "<p>Hello everyone !</p>\n<p>Following my notebook (attached to the post), you'll be able to read train data (~16GB) in much less memory (~2GB).</p>\n<p>You'll find also NaN values manipulation and the beginning of an EDA using Plotly !</p>\n<p>The notebook will be updated within future commits.</p>\n<p>Enjoy !</p>\n<p><a href=\"https://www.kaggle.com/code/tawejssh/american-express-default-prediction\" target=\"_blank\">https://www.kaggle.com/code/tawejssh/american-express-default-prediction</a></p>",
      "rawMarkdown": "Hello everyone !\n\nFollowing my notebook (attached to the post), you'll be able to read train data (~16GB) in much less memory (~2GB).\n\nYou'll find also NaN values manipulation and the beginning of an EDA using Plotly !\n\nThe notebook will be updated within future commits.\n\nEnjoy !\n\nhttps://www.kaggle.com/code/tawejssh/american-express-default-prediction",
      "votes": null
    },
    {
      "id": "1844876",
      "postDate": "07/05/2022 22:11:57",
      "content": "<p>Thank you so much, Radhouane! This was extremely helpful! 😊</p>",
      "rawMarkdown": "Thank you so much, Radhouane! This was extremely helpful! 😊",
      "votes": null
    },
    {
      "id": "1845359",
      "postDate": "07/06/2022 08:56:03",
      "content": "<p>You're welcome, I'm glad it helped!</p>",
      "rawMarkdown": "You're welcome, I'm glad it helped!",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1844876,
      "author_name": "rmgoldberg",
      "author_url": "",
      "post_date": "07/05/2022 22:11:57",
      "content": "<p>Thank you so much, Radhouane! This was extremely helpful! 😊</p>",
      "votes": null,
      "replies": [
        {
          "id": 1845359,
          "author_name": "tawejssh",
          "author_url": "",
          "post_date": "07/06/2022 08:56:03",
          "content": "<p>You're welcome, I'm glad it helped!</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "1807175": "Hello everyone !\n\nFollowing my notebook (attached to the post), you'll be able to read train data (~16GB) in much less memory (~2GB).\n\nYou'll find also NaN values manipulation and the beginning of an EDA using Plotly !\n\nThe notebook will be updated within future commits.\n\nEnjoy !\n\nhttps://www.kaggle.com/code/tawejssh/american-express-default-prediction",
    "1844876": "Thank you so much, Radhouane! This was extremely helpful! 😊",
    "1845359": "You're welcome, I'm glad it helped!"
  },
  "source": "meta"
}