{
  "id": 309718,
  "title": "Understanding the customers.csv dataset",
  "url": "/competitions/h-and-m-personalized-fashion-recommendations/discussion/309718",
  "author_name": "",
  "post_date": "2022-02-25T03:04:08.456994500Z",
  "votes": 3,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Hey All! I am just starting out with Kaggle and a beginner in the ML space. I was going over the data and was wondering what some of the column names mean and what null values in these areas mean. Here are my open queries and current understanding of the customers table:</p>\n<p>Open Questions:</p>\n<ol>\n<li>Are customers with Active Nan inactive customers?</li>\n<li>What does FN stand for?</li>\n<li>What does club_member_status Nan stand for?</li>\n</ol>\n<p>Understanding:</p>\n<ol>\n<li><p>How are Nans in FN and Active related?<br>\nCustomers with Nan in FN have Active Nan but not the other way round.</p></li>\n<li><p>How many customers have Active Nan and FN not Nan?<br>\nThere are 12526 such customers which is around 0.9% of total customers.</p></li>\n<li><p>How many customers have Active Nan?<br>\nThere are 907576 such customers which is around 66% of customers.</p></li>\n<li><p>How many customers have Nans in club_member_status?<br>\nThere are 6062 such customers which is around 0.4% of customers.</p></li>\n<li><p>How many customers have Nans in fashion_news_frequency?<br>\nThere are 16009 such customers which is around 1% of customers.</p></li>\n<li><p>What does fashion_news_frequency Nan stand for?<br>\nHypothesis is that these customers did not sign up for the newsletter. We can merge 'NONE' and \"None\" into this as well</p></li>\n<li><p>How many customers have Nans in age?<br>\nThere are 15861 such customers which is around 1% of customers.</p></li>\n</ol>",
  "messages": [
    {
      "id": "1703951",
      "postDate": "02/25/2022 03:04:08",
      "content": "<p>Hey All! I am just starting out with Kaggle and a beginner in the ML space. I was going over the data and was wondering what some of the column names mean and what null values in these areas mean. Here are my open queries and current understanding of the customers table:</p>\n<p>Open Questions:</p>\n<ol>\n<li>Are customers with Active Nan inactive customers?</li>\n<li>What does FN stand for?</li>\n<li>What does club_member_status Nan stand for?</li>\n</ol>\n<p>Understanding:</p>\n<ol>\n<li><p>How are Nans in FN and Active related?<br>\nCustomers with Nan in FN have Active Nan but not the other way round.</p></li>\n<li><p>How many customers have Active Nan and FN not Nan?<br>\nThere are 12526 such customers which is around 0.9% of total customers.</p></li>\n<li><p>How many customers have Active Nan?<br>\nThere are 907576 such customers which is around 66% of customers.</p></li>\n<li><p>How many customers have Nans in club_member_status?<br>\nThere are 6062 such customers which is around 0.4% of customers.</p></li>\n<li><p>How many customers have Nans in fashion_news_frequency?<br>\nThere are 16009 such customers which is around 1% of customers.</p></li>\n<li><p>What does fashion_news_frequency Nan stand for?<br>\nHypothesis is that these customers did not sign up for the newsletter. We can merge 'NONE' and \"None\" into this as well</p></li>\n<li><p>How many customers have Nans in age?<br>\nThere are 15861 such customers which is around 1% of customers.</p></li>\n</ol>",
      "rawMarkdown": "Hey All! I am just starting out with Kaggle and a beginner in the ML space. I was going over the data and was wondering what some of the column names mean and what null values in these areas mean. Here are my open queries and current understanding of the customers table:\n\n\nOpen Questions:\n1. Are customers with Active Nan inactive customers?\n2. What does FN stand for?\n3. What does club_member_status Nan stand for?\n\n\nUnderstanding:\n1. How are Nans in FN and Active related?\nCustomers with Nan in FN have Active Nan but not the other way round.\n\n2. How many customers have Active Nan and FN not Nan?\nThere are 12526 such customers which is around 0.9% of total customers.\n\n3. How many customers have Active Nan?\nThere are 907576 such customers which is around 66% of customers.\n\n4. How many customers have Nans in club_member_status?\nThere are 6062 such customers which is around 0.4% of customers.\n\n5. How many customers have Nans in fashion_news_frequency?\nThere are 16009 such customers which is around 1% of customers.\n\n6. What does fashion_news_frequency Nan stand for?\nHypothesis is that these customers did not sign up for the newsletter. We can merge 'NONE' and \"None\" into this as well\n\n7. How many customers have Nans in age?\nThere are 15861 such customers which is around 1% of customers.",
      "votes": null
    },
    {
      "id": "1724905",
      "postDate": "03/16/2022 16:06:38",
      "content": "<p>Hi <a href=\"https://www.kaggle.com/pratijayguha\" target=\"_blank\">@pratijayguha</a>, as the host clarifies in this <a href=\"https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/305952#1684481\" target=\"_blank\">thread</a>,<br><br>\n<code>FN is if a customer get Fashion News newsletter</code>.<br><br>\nThanks for sharing, and hope this helps.</p>",
      "rawMarkdown": "Hi @pratijayguha, as the host clarifies in this [thread](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/305952#1684481),<br>\n`FN is if a customer get Fashion News newsletter`.<br>\nThanks for sharing, and hope this helps.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1724905,
      "author_name": "abaojiang",
      "author_url": "",
      "post_date": "03/16/2022 16:06:38",
      "content": "<p>Hi <a href=\"https://www.kaggle.com/pratijayguha\" target=\"_blank\">@pratijayguha</a>, as the host clarifies in this <a href=\"https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/305952#1684481\" target=\"_blank\">thread</a>,<br><br>\n<code>FN is if a customer get Fashion News newsletter</code>.<br><br>\nThanks for sharing, and hope this helps.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1703951": "Hey All! I am just starting out with Kaggle and a beginner in the ML space. I was going over the data and was wondering what some of the column names mean and what null values in these areas mean. Here are my open queries and current understanding of the customers table:\n\n\nOpen Questions:\n1. Are customers with Active Nan inactive customers?\n2. What does FN stand for?\n3. What does club_member_status Nan stand for?\n\n\nUnderstanding:\n1. How are Nans in FN and Active related?\nCustomers with Nan in FN have Active Nan but not the other way round.\n\n2. How many customers have Active Nan and FN not Nan?\nThere are 12526 such customers which is around 0.9% of total customers.\n\n3. How many customers have Active Nan?\nThere are 907576 such customers which is around 66% of customers.\n\n4. How many customers have Nans in club_member_status?\nThere are 6062 such customers which is around 0.4% of customers.\n\n5. How many customers have Nans in fashion_news_frequency?\nThere are 16009 such customers which is around 1% of customers.\n\n6. What does fashion_news_frequency Nan stand for?\nHypothesis is that these customers did not sign up for the newsletter. We can merge 'NONE' and \"None\" into this as well\n\n7. How many customers have Nans in age?\nThere are 15861 such customers which is around 1% of customers.",
    "1724905": "Hi @pratijayguha, as the host clarifies in this [thread](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/305952#1684481),<br>\n`FN is if a customer get Fashion News newsletter`.<br>\nThanks for sharing, and hope this helps."
  },
  "source": "meta"
}