{
  "id": 309789,
  "title": "Get \"gender\" feature in your analysis",
  "url": "/competitions/h-and-m-personalized-fashion-recommendations/discussion/309789",
  "author_name": "",
  "post_date": "2022-02-25T14:25:29.811090Z",
  "votes": 4,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Gender is important customer feature.<br>\nBut it dosen't exist in the customer dataset.</p>\n<p>I focus on  \"index_group_name\" in articles dataset. <br>\nThe feature \"index_group_name\" consists of 5 labels, \"Ladieswear\", \"Menswear\", \"Divided\" , \"Sport\" and \"Baby/Children\".</p>\n<p>For example, if a customer bought Ladieswear, the customer's gender was most likely female.</p>\n<h2>Overview of method</h2>\n<ul>\n<li>Get \"index_group_name\" correspond to \"customer_id\" in  \"transactions_train.csv\".</li>\n<li>Add feature \"index_group_name\" submission_dataframe on customer_id.</li>\n<li>Create prediction  for each index_group_name customers.</li>\n</ul>\n<h2>Share sample code below</h2>\n<p><a href=\"https://www.kaggle.com/shigeeeru/add-gender-feature\" target=\"_blank\">https://www.kaggle.com/shigeeeru/add-gender-feature</a></p>\n<p>I'd like to discuss this topic, so please comment and upvote!</p>\n<p>=================================================</p>\n<h3>Add information</h3>\n<p>More discussion <a href=\"https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555\" target=\"_blank\">here</a></p>",
  "messages": [
    {
      "id": "1704448",
      "postDate": "02/25/2022 14:25:29",
      "content": "<p>Gender is important customer feature.<br>\nBut it dosen't exist in the customer dataset.</p>\n<p>I focus on  \"index_group_name\" in articles dataset. <br>\nThe feature \"index_group_name\" consists of 5 labels, \"Ladieswear\", \"Menswear\", \"Divided\" , \"Sport\" and \"Baby/Children\".</p>\n<p>For example, if a customer bought Ladieswear, the customer's gender was most likely female.</p>\n<h2>Overview of method</h2>\n<ul>\n<li>Get \"index_group_name\" correspond to \"customer_id\" in  \"transactions_train.csv\".</li>\n<li>Add feature \"index_group_name\" submission_dataframe on customer_id.</li>\n<li>Create prediction  for each index_group_name customers.</li>\n</ul>\n<h2>Share sample code below</h2>\n<p><a href=\"https://www.kaggle.com/shigeeeru/add-gender-feature\" target=\"_blank\">https://www.kaggle.com/shigeeeru/add-gender-feature</a></p>\n<p>I'd like to discuss this topic, so please comment and upvote!</p>\n<p>=================================================</p>\n<h3>Add information</h3>\n<p>More discussion <a href=\"https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555\" target=\"_blank\">here</a></p>",
      "rawMarkdown": "Gender is important customer feature.\nBut it dosen't exist in the customer dataset.\n\nI focus on  \"index_group_name\" in articles dataset. \nThe feature \"index_group_name\" consists of 5 labels, \"Ladieswear\", \"Menswear\", \"Divided\" , \"Sport\" and \"Baby/Children\".\n\nFor example, if a customer bought Ladieswear, the customer's gender was most likely female.\n\n##Overview of method\n- Get \"index_group_name\" correspond to \"customer_id\" in  \"transactions_train.csv\".\n- Add feature \"index_group_name\" submission_dataframe on customer_id.\n- Create prediction  for each index_group_name customers.\n\n##Share sample code below\nhttps://www.kaggle.com/shigeeeru/add-gender-feature\n\nI'd like to discuss this topic, so please comment and upvote!\n\n=================================================\n### Add information\nMore discussion [here](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555)",
      "votes": null
    },
    {
      "id": "1704857",
      "postDate": "02/25/2022 22:47:42",
      "content": "<p>Thanks for sharing. More discussion <a href=\"https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555\" target=\"_blank\">here</a>. You can also use the <code>section_name</code> feature</p>",
      "rawMarkdown": "Thanks for sharing. More discussion [here][1]. You can also use the `section_name` feature\n\n[1]: https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555",
      "votes": null
    },
    {
      "id": "1704878",
      "postDate": "02/26/2022 00:33:11",
      "content": "<p>Thanks for your comment !! I miss this discussion. ( ；∀；)</p>",
      "rawMarkdown": "Thanks for your comment !! I miss this discussion. ( ；∀；)",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1704857,
      "author_name": "cdeotte",
      "author_url": "",
      "post_date": "02/25/2022 22:47:42",
      "content": "<p>Thanks for sharing. More discussion <a href=\"https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555\" target=\"_blank\">here</a>. You can also use the <code>section_name</code> feature</p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1704878,
      "author_name": "shigeeeru",
      "author_url": "",
      "post_date": "02/26/2022 00:33:11",
      "content": "<p>Thanks for your comment !! I miss this discussion. ( ；∀；)</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1704448": "Gender is important customer feature.\nBut it dosen't exist in the customer dataset.\n\nI focus on  \"index_group_name\" in articles dataset. \nThe feature \"index_group_name\" consists of 5 labels, \"Ladieswear\", \"Menswear\", \"Divided\" , \"Sport\" and \"Baby/Children\".\n\nFor example, if a customer bought Ladieswear, the customer's gender was most likely female.\n\n##Overview of method\n- Get \"index_group_name\" correspond to \"customer_id\" in  \"transactions_train.csv\".\n- Add feature \"index_group_name\" submission_dataframe on customer_id.\n- Create prediction  for each index_group_name customers.\n\n##Share sample code below\nhttps://www.kaggle.com/shigeeeru/add-gender-feature\n\nI'd like to discuss this topic, so please comment and upvote!\n\n=================================================\n### Add information\nMore discussion [here](https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555)",
    "1704857": "Thanks for sharing. More discussion [here][1]. You can also use the `section_name` feature\n\n[1]: https://www.kaggle.com/c/h-and-m-personalized-fashion-recommendations/discussion/308555",
    "1704878": "Thanks for your comment !! I miss this discussion. ( ；∀；)"
  },
  "source": "meta"
}