{
  "id": 333333,
  "title": "Column Documentation?",
  "url": "/competitions/amex-default-prediction/discussion/333333",
  "author_name": "",
  "post_date": "2022-06-26T01:43:09.158455200Z",
  "votes": 2,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hi, Is there any column documentation available to better understand what each metric means?</p>",
  "messages": [
    {
      "id": "1833403",
      "postDate": "06/26/2022 01:43:09",
      "content": "<p>Hi, Is there any column documentation available to better understand what each metric means?</p>",
      "rawMarkdown": "Hi, Is there any column documentation available to better understand what each metric means?",
      "votes": null
    },
    {
      "id": "1833677",
      "postDate": "06/26/2022 08:03:06",
      "content": "<p>There's the official documentation for features…</p>\n<blockquote>\n  <p>The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:</p>\n  <p>D_* = Delinquency variables<br>\n  S_* = Spend variables<br>\n  P_* = Payment variables<br>\n  B_* = Balance variables<br>\n  R_* = Risk variables<br>\n  with the following features being categorical:</p>\n  <p>['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']`</p>\n</blockquote>\n<p>And I'd recommend using the discussion and code search features in Kaggle to see if similar questions have been asked..</p>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14756%2F9454e98e5bd9425b62346ed07c64b9ca%2FAmex_Feature.JPG?generation=1656230557810017&amp;alt=media\" alt=\"\"> </p>",
      "rawMarkdown": "There's the official documentation for features...\n\n> The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:\n>\nD_* = Delinquency variables\nS_* = Spend variables\nP_* = Payment variables\nB_* = Balance variables\nR_* = Risk variables\nwith the following features being categorical:\n>\n['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']`\n\nAnd I'd recommend using the discussion and code search features in Kaggle to see if similar questions have been asked..\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14756%2F9454e98e5bd9425b62346ed07c64b9ca%2FAmex_Feature.JPG?generation=1656230557810017&alt=media)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1833677,
      "author_name": "nigelcarpenter",
      "author_url": "",
      "post_date": "06/26/2022 08:03:06",
      "content": "<p>There's the official documentation for features…</p>\n<blockquote>\n  <p>The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:</p>\n  <p>D_* = Delinquency variables<br>\n  S_* = Spend variables<br>\n  P_* = Payment variables<br>\n  B_* = Balance variables<br>\n  R_* = Risk variables<br>\n  with the following features being categorical:</p>\n  <p>['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']`</p>\n</blockquote>\n<p>And I'd recommend using the discussion and code search features in Kaggle to see if similar questions have been asked..</p>\n<p><img src=\"https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14756%2F9454e98e5bd9425b62346ed07c64b9ca%2FAmex_Feature.JPG?generation=1656230557810017&amp;alt=media\" alt=\"\"> </p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1833403": "Hi, Is there any column documentation available to better understand what each metric means?",
    "1833677": "There's the official documentation for features...\n\n> The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:\n>\nD_* = Delinquency variables\nS_* = Spend variables\nP_* = Payment variables\nB_* = Balance variables\nR_* = Risk variables\nwith the following features being categorical:\n>\n['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']`\n\nAnd I'd recommend using the discussion and code search features in Kaggle to see if similar questions have been asked..\n\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F14756%2F9454e98e5bd9425b62346ed07c64b9ca%2FAmex_Feature.JPG?generation=1656230557810017&alt=media)"
  },
  "source": "meta"
}