{
  "id": 555956,
  "title": "Sample Notebook Submission",
  "url": "/competitions/beginners-hypothesis-25/discussion/555956",
  "author_name": "kaustubh",
  "post_date": "2025-01-10T10:06:30.142000",
  "votes": 0,
  "comment_count": 0,
  "views": 0,
  "content": "<p>For clarity, a sample notebook has been created based on simple linear regression to make the predictions and submit the csv. You are advised to go through the code properly, and it would serve as a baseline for your future work in the competition.</p>\n<p>In case of any queries, ask in the discussions forums or on the WhatsApp group.</p>\n<p>You can download the .ipynb file attached below, and run it in your local environment, or upload it on kaggle and use it after uploading the dataset.</p>",
  "messages": [
    {
      "id": 3092977,
      "postDate": "2025-01-10T10:06:30.143Z",
      "content": "<p>For clarity, a sample notebook has been created based on simple linear regression to make the predictions and submit the csv. You are advised to go through the code properly, and it would serve as a baseline for your future work in the competition.</p>\n<p>In case of any queries, ask in the discussions forums or on the WhatsApp group.</p>\n<p>You can download the .ipynb file attached below, and run it in your local environment, or upload it on kaggle and use it after uploading the dataset.</p>",
      "rawMarkdown": "For clarity, a sample notebook has been created based on simple linear regression to make the predictions and submit the csv. You are advised to go through the code properly, and it would serve as a baseline for your future work in the competition.\n\nIn case of any queries, ask in the discussions forums or on the WhatsApp group.\n\nYou can download the .ipynb file attached below, and run it in your local environment, or upload it on kaggle and use it after uploading the dataset.\n\n"
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "3092977": "For clarity, a sample notebook has been created based on simple linear regression to make the predictions and submit the csv. You are advised to go through the code properly, and it would serve as a baseline for your future work in the competition.\n\nIn case of any queries, ask in the discussions forums or on the WhatsApp group.\n\nYou can download the .ipynb file attached below, and run it in your local environment, or upload it on kaggle and use it after uploading the dataset.\n\n"
  }
}