{
  "id": 407233,
  "title": "submission code",
  "url": "/competitions/predict-student-performance-from-game-play/discussion/407233",
  "author_name": "",
  "post_date": "2023-05-05T16:09:17.727269200Z",
  "votes": 1,
  "comment_count": 3,
  "views": 0,
  "content": "<p>I am new to kaggle competition so can someone tell me what this code do and why we use it for submission , and should i make feature engineer for the test data inside it?</p>\n<p>import jo_wilder<br>\nenv = jo_wilder.make_env()<br>\niter_test = env.iter_test()</p>\n<p>counter = 0</p>\n<h1>The API will deliver two dataframes in this specific order,</h1>\n<h1>for every session+level grouping (one group per session for each checkpoint)</h1>\n<p>for (sample_submission, test) in iter_test:<br>\n    if counter == 0:<br>\n        print(sample_submission.head())<br>\n        print(test.head())<br>\n        print(test.shape)</p>\n<pre><code>## users make predictions here using the test data\nsample_submission['correct'] = 0\n\n## env.predict appends the session+level sample_submission to the overall\n## submission\nenv.predict(sample_submission)\ncounter += 1\n</code></pre>",
  "messages": [
    {
      "id": "2247018",
      "postDate": "05/05/2023 16:09:17",
      "content": "<p>I am new to kaggle competition so can someone tell me what this code do and why we use it for submission , and should i make feature engineer for the test data inside it?</p>\n<p>import jo_wilder<br>\nenv = jo_wilder.make_env()<br>\niter_test = env.iter_test()</p>\n<p>counter = 0</p>\n<h1>The API will deliver two dataframes in this specific order,</h1>\n<h1>for every session+level grouping (one group per session for each checkpoint)</h1>\n<p>for (sample_submission, test) in iter_test:<br>\n    if counter == 0:<br>\n        print(sample_submission.head())<br>\n        print(test.head())<br>\n        print(test.shape)</p>\n<pre><code>## users make predictions here using the test data\nsample_submission['correct'] = 0\n\n## env.predict appends the session+level sample_submission to the overall\n## submission\nenv.predict(sample_submission)\ncounter += 1\n</code></pre>",
      "rawMarkdown": "I am new to kaggle competition so can someone tell me what this code do and why we use it for submission , and should i make feature engineer for the test data inside it?\n\nimport jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()\n \ncounter = 0\n# The API will deliver two dataframes in this specific order,\n# for every session+level grouping (one group per session for each checkpoint)\nfor (sample_submission, test) in iter_test:\n    if counter == 0:\n        print(sample_submission.head())\n        print(test.head())\n        print(test.shape)\n        \n    ## users make predictions here using the test data\n    sample_submission['correct'] = 0\n    \n    ## env.predict appends the session+level sample_submission to the overall\n    ## submission\n    env.predict(sample_submission)\n    counter += 1",
      "votes": null
    },
    {
      "id": "2247467",
      "postDate": "05/06/2023 03:19:00",
      "content": "<p>right. you should perform feature engineering on the test data in a similar way as you did for the training data. You can use the feature_engineer function defined in <a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/amaanansari09/neural-network-keras-for-jo-wilder-competition</a> as a reference.</p>",
      "rawMarkdown": "right. you should perform feature engineering on the test data in a similar way as you did for the training data. You can use the feature_engineer function defined in [https://www.kaggle.com/code/amaanansari09/neural-network-keras-for-jo-wilder-competition](url) as a reference.",
      "votes": null
    },
    {
      "id": "2247470",
      "postDate": "05/06/2023 03:24:39",
      "content": "<p>Thank you for your reply but why we use this code for submission and what does it mean?</p>",
      "rawMarkdown": "Thank you for your reply but why we use this code for submission and what does it mean?",
      "votes": null
    },
    {
      "id": "2321765",
      "postDate": "06/28/2023 21:14:46",
      "content": "<p>Hey, Did you get the answer??</p>",
      "rawMarkdown": "Hey, Did you get the answer??",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 2247467,
      "author_name": "yus002",
      "author_url": "",
      "post_date": "05/06/2023 03:19:00",
      "content": "<p>right. you should perform feature engineering on the test data in a similar way as you did for the training data. You can use the feature_engineer function defined in <a href=\"url\" target=\"_blank\">https://www.kaggle.com/code/amaanansari09/neural-network-keras-for-jo-wilder-competition</a> as a reference.</p>",
      "votes": null,
      "replies": [
        {
          "id": 2247470,
          "author_name": "mohamedbotaleb",
          "author_url": "",
          "post_date": "05/06/2023 03:24:39",
          "content": "<p>Thank you for your reply but why we use this code for submission and what does it mean?</p>",
          "votes": null,
          "replies": [
            {
              "id": 2321765,
              "author_name": "cool1ved",
              "author_url": "",
              "post_date": "06/28/2023 21:14:46",
              "content": "<p>Hey, Did you get the answer??</p>",
              "votes": null,
              "replies": []
            }
          ]
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "2247018": "I am new to kaggle competition so can someone tell me what this code do and why we use it for submission , and should i make feature engineer for the test data inside it?\n\nimport jo_wilder\nenv = jo_wilder.make_env()\niter_test = env.iter_test()\n \ncounter = 0\n# The API will deliver two dataframes in this specific order,\n# for every session+level grouping (one group per session for each checkpoint)\nfor (sample_submission, test) in iter_test:\n    if counter == 0:\n        print(sample_submission.head())\n        print(test.head())\n        print(test.shape)\n        \n    ## users make predictions here using the test data\n    sample_submission['correct'] = 0\n    \n    ## env.predict appends the session+level sample_submission to the overall\n    ## submission\n    env.predict(sample_submission)\n    counter += 1",
    "2247467": "right. you should perform feature engineering on the test data in a similar way as you did for the training data. You can use the feature_engineer function defined in [https://www.kaggle.com/code/amaanansari09/neural-network-keras-for-jo-wilder-competition](url) as a reference.",
    "2247470": "Thank you for your reply but why we use this code for submission and what does it mean?",
    "2321765": "Hey, Did you get the answer??"
  },
  "source": "meta"
}