{
  "id": 90200,
  "title": "LANL Fresh EDA kernel",
  "url": "/competitions/LANL-Earthquake-Prediction/discussion/90200",
  "author_name": "",
  "post_date": "2019-04-21T16:36:29.562346600Z",
  "votes": 4,
  "comment_count": 4,
  "views": 0,
  "content": "<p>Hello everyone !\nCheck out this <a href=\"https://www.kaggle.com/tarunpaparaju/lanl-earthquake-prediction-fresh-eda\">new kernel</a> in which I explore a new method of extracting features from signal data (which hasn't really been used in public kernels) and some new features and EDA techniques. I hope you all find it useful :)</p>",
  "messages": [
    {
      "id": "520724",
      "postDate": "04/21/2019 16:36:29",
      "content": "<p>Hello everyone !\nCheck out this <a href=\"https://www.kaggle.com/tarunpaparaju/lanl-earthquake-prediction-fresh-eda\">new kernel</a> in which I explore a new method of extracting features from signal data (which hasn't really been used in public kernels) and some new features and EDA techniques. I hope you all find it useful :)</p>",
      "rawMarkdown": "Hello everyone !\nCheck out this [new kernel](https://www.kaggle.com/tarunpaparaju/lanl-earthquake-prediction-fresh-eda) in which I explore a new method of extracting features from signal data (which hasn't really been used in public kernels) and some new features and EDA techniques. I hope you all find it useful :)",
      "votes": null
    },
    {
      "id": "520732",
      "postDate": "04/21/2019 16:54:41",
      "content": "<p>Thanks for sharing.</p>",
      "rawMarkdown": "Thanks for sharing.",
      "votes": null
    },
    {
      "id": "520735",
      "postDate": "04/21/2019 16:55:47",
      "content": "<p>It's my pleasure to share, CPMP ! Hope you find it useful :)</p>",
      "rawMarkdown": "It's my pleasure to share, CPMP ! Hope you find it useful :)",
      "votes": null
    },
    {
      "id": "520743",
      "postDate": "04/21/2019 17:01:18",
      "content": "<p><strong>Thanks for sharing <a href=\"/tarunpaparaju\">@tarunpaparaju</a></strong> </p>\n\n<p><strong>Eager to know what was the increment in CV/LB it gave with respect to your previous submission.</strong></p>\n\n<p><strong>Thank you.</strong></p>",
      "rawMarkdown": "**Thanks for sharing @tarunpaparaju** \n\n**Eager to know what was the increment in CV/LB it gave with respect to your previous submission.**\n\n**Thank you.**",
      "votes": null
    },
    {
      "id": "520766",
      "postDate": "04/21/2019 17:40:42",
      "content": "<p>I'll tell you if I find it useful, for sure.</p>",
      "rawMarkdown": "I'll tell you if I find it useful, for sure.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 520732,
      "author_name": "cpmpml",
      "author_url": "",
      "post_date": "04/21/2019 16:54:41",
      "content": "<p>Thanks for sharing.</p>",
      "votes": null,
      "replies": [
        {
          "id": 520735,
          "author_name": "tarunpaparaju",
          "author_url": "",
          "post_date": "04/21/2019 16:55:47",
          "content": "<p>It's my pleasure to share, CPMP ! Hope you find it useful :)</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 520766,
          "author_name": "cpmpml",
          "author_url": "",
          "post_date": "04/21/2019 17:40:42",
          "content": "<p>I'll tell you if I find it useful, for sure.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 520743,
      "author_name": "akashravichandran",
      "author_url": "",
      "post_date": "04/21/2019 17:01:18",
      "content": "<p><strong>Thanks for sharing <a href=\"/tarunpaparaju\">@tarunpaparaju</a></strong> </p>\n\n<p><strong>Eager to know what was the increment in CV/LB it gave with respect to your previous submission.</strong></p>\n\n<p><strong>Thank you.</strong></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "520724": "Hello everyone !\nCheck out this [new kernel](https://www.kaggle.com/tarunpaparaju/lanl-earthquake-prediction-fresh-eda) in which I explore a new method of extracting features from signal data (which hasn't really been used in public kernels) and some new features and EDA techniques. I hope you all find it useful :)",
    "520732": "Thanks for sharing.",
    "520735": "It's my pleasure to share, CPMP ! Hope you find it useful :)",
    "520743": "**Thanks for sharing @tarunpaparaju** \n\n**Eager to know what was the increment in CV/LB it gave with respect to your previous submission.**\n\n**Thank you.**",
    "520766": "I'll tell you if I find it useful, for sure."
  },
  "source": "meta"
}