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  "fetched_at": "2026-10-19T19:03:55Z",
  "source_url": "https://www.kaggle.com/competitions/copy-of-gdsc-nsut-recruitment-2023/data",
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      "content": "## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n\n## Columns\n\n*   `id` - unique id of a row\n*   `F_X` - Feature X [0 - 39]\n*   `target` - Column to be predicted, not present in **test.csv**",
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      "name": "Description",
      "content": "\n# GDSC-NSUT ML Department Recruitment 2023\n\n## Task: Regression\nThe task is to predict the values of the \"target\" column using machine learning algorithms.\n\n## Rules:\n1. Individual submissions are required, teaming is not permitted.\n2. The competition will run from February 08th, 2023 to February 17th, 2023.\n3. The top 20 ranked submissions in the Private Leaderboard will be selected for an interview.\n4. You can submit the `solution.csv` file either by uploading it or by using a notebook. \n5. In addition to the top 20, five submissions will be selected based on the data visualization and analysis performed on the given dataset. You can submit your Jupyter notebooks through Kaggle or share them with department leads Poojan and Chaitanya (contact information can be found in the Google doc).\n6. Googling is allowed, so feel free to explore as much as you can. \n7. A tutorial notebook has been provided and you are free to use it and make changes.\n8. Any questions can be asked in the discord server or the discussion panel for this competition.\n9. Learning resources have been provided in the Google doc.\n\nAll the best!!!",
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      "name": "Evaluation",
      "content": "## Evaluation Metric\nThe evaluation metric for this competition is the Root Mean Squared Error (RMSE).\n\nRMSE is a commonly used metric for evaluating the accuracy of a regression model. It measures the average difference between the predicted values and the actual values. The lower the RMSE, the better the model is at predicting the actual values.\nhttps://en.wikipedia.org/wiki/Root-mean-square_deviation\n\n## Submission Format\nFor every id in the test dataset, the submission file should contain two columns: `id` and `target`. The file should have the following format, including a header:\n\n```\nid,target\n10000,5\n10001,5\n10002,5\n10003,5\n10004,5\n10005,5\n. . .\n```",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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