{
  "id": 475084,
  "title": "Home Credit - Credit Risk Model Stability - Discussion Topic",
  "url": "/competitions/home-credit-credit-risk-model-stability/discussion/475084",
  "author_name": "",
  "post_date": "2024-02-07T03:03:13.369000",
  "votes": -18,
  "comment_count": 2,
  "views": 0,
  "content": "<p>import pandas as pd</p>\n<h1>Assuming 'predictions' is a DataFrame with columns 'id' and 'target'</h1>\n<h1>'id' represents the unique identifier for each test instance, and 'target' is the predicted value</h1>\n<p>df1 = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")<br>\nprint(df1)</p>\n<p>predictions =pd.concat([df1['case_id'],df1['score']], axis=1)</p>\n<h1>Create a submission  DataFrame</h1>\n<p>submission_df = pd.DataFrame({<br>\n    'case_id': predictions['case_id'],<br>\n    'score':predictions['score'],  <br>\n})</p>\n<h1>Save the submission DataFrame to a CSV file</h1>\n<p>submission_df.to_csv('submission.csv', index=False)</p>",
  "messages": [
    {
      "id": 2668245,
      "postDate": "2024-02-25T15:19:01.917Z",
      "content": "<p>Hi <a href=\"https://www.kaggle.com/gopalakrishnankumar\" target=\"_blank\">@gopalakrishnankumar</a>, I just took a peek at your posting history:</p>\n<ul>\n<li>-95    net votes</li>\n<li>-3.7    votes / post</li>\n</ul>\n<p>I suspect you might be deliberate in your actions. </p>\n<p>If not, you need to improve your \"discussion game\".</p>\n<p>Look at what kinds of posts are generating interest and engagement, and contribute your thoughts and insights. Sharing data analysis or feature engineering work will demonstrate your knowledge far more than a link to an online cert.</p>\n<p>Show, don't tell.</p>\n<p>Best of luck,</p>",
      "rawMarkdown": "Hi @gopalakrishnankumar, I just took a peek at your posting history:\n\n-  -95\tnet votes\n-  -3.7\tvotes / post\n\nI suspect you might be deliberate in your actions. \n\nIf not, you need to improve your \"discussion game\".\n\nLook at what kinds of posts are generating interest and engagement, and contribute your thoughts and insights. Sharing data analysis or feature engineering work will demonstrate your knowledge far more than a link to an online cert.\n\nShow, don't tell.\n\nBest of luck,",
      "votes": 1
    },
    {
      "id": 2640631,
      "postDate": "2024-02-07T03:03:13.370Z",
      "content": "<p>import pandas as pd</p>\n<h1>Assuming 'predictions' is a DataFrame with columns 'id' and 'target'</h1>\n<h1>'id' represents the unique identifier for each test instance, and 'target' is the predicted value</h1>\n<p>df1 = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")<br>\nprint(df1)</p>\n<p>predictions =pd.concat([df1['case_id'],df1['score']], axis=1)</p>\n<h1>Create a submission  DataFrame</h1>\n<p>submission_df = pd.DataFrame({<br>\n    'case_id': predictions['case_id'],<br>\n    'score':predictions['score'],  <br>\n})</p>\n<h1>Save the submission DataFrame to a CSV file</h1>\n<p>submission_df.to_csv('submission.csv', index=False)</p>",
      "rawMarkdown": "import pandas as pd\n\n# Assuming 'predictions' is a DataFrame with columns 'id' and 'target'\n# 'id' represents the unique identifier for each test instance, and 'target' is the predicted value\n\ndf1 = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")\nprint(df1)\n\npredictions =pd.concat([df1['case_id'],df1['score']], axis=1)\n\n\n# Create a submission  DataFrame\nsubmission_df = pd.DataFrame({\n    'case_id': predictions['case_id'],\n    'score':predictions['score'],  \n})\n\n# Save the submission DataFrame to a CSV file\nsubmission_df.to_csv('submission.csv', index=False)\n",
      "votes": -18
    },
    {
      "id": 2666529,
      "postDate": "2024-02-24T12:42:48.357Z",
      "rawMarkdown": "",
      "votes": -2,
      "isDeleted": true
    }
  ],
  "comments": [
    {
      "id": 2668245,
      "author_name": "paddykb",
      "author_url": "",
      "post_date": "2024-02-25T15:19:01.917000",
      "content": "<p>Hi <a href=\"https://www.kaggle.com/gopalakrishnankumar\" target=\"_blank\">@gopalakrishnankumar</a>, I just took a peek at your posting history:</p>\n<ul>\n<li>-95    net votes</li>\n<li>-3.7    votes / post</li>\n</ul>\n<p>I suspect you might be deliberate in your actions. </p>\n<p>If not, you need to improve your \"discussion game\".</p>\n<p>Look at what kinds of posts are generating interest and engagement, and contribute your thoughts and insights. Sharing data analysis or feature engineering work will demonstrate your knowledge far more than a link to an online cert.</p>\n<p>Show, don't tell.</p>\n<p>Best of luck,</p>",
      "votes": 1,
      "replies": []
    },
    {
      "id": 2666529,
      "author_name": "",
      "author_url": "",
      "post_date": "2024-02-24T12:42:48.357000",
      "content": "",
      "votes": -2,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "2668245": "Hi @gopalakrishnankumar, I just took a peek at your posting history:\n\n-  -95\tnet votes\n-  -3.7\tvotes / post\n\nI suspect you might be deliberate in your actions. \n\nIf not, you need to improve your \"discussion game\".\n\nLook at what kinds of posts are generating interest and engagement, and contribute your thoughts and insights. Sharing data analysis or feature engineering work will demonstrate your knowledge far more than a link to an online cert.\n\nShow, don't tell.\n\nBest of luck,",
    "2640631": "import pandas as pd\n\n# Assuming 'predictions' is a DataFrame with columns 'id' and 'target'\n# 'id' represents the unique identifier for each test instance, and 'target' is the predicted value\n\ndf1 = pd.read_csv(\"/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv\")\nprint(df1)\n\npredictions =pd.concat([df1['case_id'],df1['score']], axis=1)\n\n\n# Create a submission  DataFrame\nsubmission_df = pd.DataFrame({\n    'case_id': predictions['case_id'],\n    'score':predictions['score'],  \n})\n\n# Save the submission DataFrame to a CSV file\nsubmission_df.to_csv('submission.csv', index=False)\n",
    "2666529": ""
  }
}