{
  "id": 24838,
  "title": "strange date feature values",
  "url": "/competitions/bosch-production-line-performance/discussion/24838",
  "author_name": "",
  "post_date": "2016-10-27T15:51:27.543Z",
  "votes": null,
  "comment_count": 1,
  "views": 409,
  "content": "<p>After exploring date features, i see the values is like '1143.68', '999.59', '1135.66', '1135.61', '405.7', but from the competition data description, it says &quot;The date features provide a timestamp for when each measurement was taken&quot;, this doesn't seem to be a valid timestamp.</p>",
  "messages": [
    {
      "id": "141629",
      "postDate": "10/27/2016 15:51:27",
      "content": "<p>After exploring date features, i see the values is like '1143.68', '999.59', '1135.66', '1135.61', '405.7', but from the competition data description, it says &quot;The date features provide a timestamp for when each measurement was taken&quot;, this doesn't seem to be a valid timestamp.</p>",
      "rawMarkdown": "After exploring date features, i see the values is like '1143.68', '999.59', '1135.66', '1135.61', '405.7', but from the competition data description, it says \"The date features provide a timestamp for when each measurement was taken\", this doesn't seem to be a valid timestamp.",
      "votes": null
    },
    {
      "id": "141633",
      "postDate": "10/27/2016 16:00:26",
      "content": "<p>Indeed they are strange. My guess is that 0.01 ~ 6 min. See\n<a href=\"https://www.kaggle.com/gaborfodor/bosch-production-line-performance/notebookd19d11e4f2\">https://www.kaggle.com/gaborfodor/bosch-production-line-performance/notebookd19d11e4f2</a></p>\n\n<p>The numeric features the category features and the file orders are strange too :)\nCheck the forums and kernels there are plenty of useful findings there.</p>",
      "rawMarkdown": "Indeed they are strange. My guess is that 0.01 ~ 6 min. See\r\nhttps://www.kaggle.com/gaborfodor/bosch-production-line-performance/notebookd19d11e4f2\r\n\r\nThe numeric features the category features and the file orders are strange too :)\r\nCheck the forums and kernels there are plenty of useful findings there.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 141633,
      "author_name": "gaborfodor",
      "author_url": "",
      "post_date": "10/27/2016 16:00:26",
      "content": "<p>Indeed they are strange. My guess is that 0.01 ~ 6 min. See\n<a href=\"https://www.kaggle.com/gaborfodor/bosch-production-line-performance/notebookd19d11e4f2\">https://www.kaggle.com/gaborfodor/bosch-production-line-performance/notebookd19d11e4f2</a></p>\n\n<p>The numeric features the category features and the file orders are strange too :)\nCheck the forums and kernels there are plenty of useful findings there.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "141629": "After exploring date features, i see the values is like '1143.68', '999.59', '1135.66', '1135.61', '405.7', but from the competition data description, it says \"The date features provide a timestamp for when each measurement was taken\", this doesn't seem to be a valid timestamp.",
    "141633": "Indeed they are strange. My guess is that 0.01 ~ 6 min. See\r\nhttps://www.kaggle.com/gaborfodor/bosch-production-line-performance/notebookd19d11e4f2\r\n\r\nThe numeric features the category features and the file orders are strange too :)\r\nCheck the forums and kernels there are plenty of useful findings there."
  },
  "source": "meta"
}