{
  "fetched_at": "2026-10-19T20:07:23Z",
  "source_url": "https://www.kaggle.com/competitions/fall-2024-birth-weight-prediction/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2>Dataset Description</h2>\nIn the “dataset/” you will find two csv and a txt files: \n<ul>\n<li>baby-weights-dataset.csv</li>\n<ul><li>It has 101400 rows (samples) with 37 columns (variables). Each sample represents a case of a new-born. Very last column is \"BWEIGHT\", that tells the true weight of the new-born (in lbs unit). Actually, this needs to be considered as the target variable here. </li></ul>\n<li>data-description.txt</li>\n<ul><li>Here, you will find the names of the 37 variables used in the dataset above. And, the source of the dataset did not offer me description of every single of them. But, after studying about them, I could elaborate only few of them. Please pardon my laziness. Okay, this file contains few descriptions for the variables. All the rest are mostly talking about the Mother's medical history and all. No big deal, I guess, for you to work with these variables without knowing their meaning.</li></ul>\n<li>judge-without-labels.csv</li>\n<ul></li>This is an interesting file. It contains new samples: additional 2000 rows with 36 columns (without the BWEIGHT target column). Predict BWEIGHT values of these 2000 samples. We (me and the TA) will evaluate that as well.</li></ul>\n</ul>\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "I am quoting from the WebMD article:\n“A new baby's gender, name, time of birth, and birth weight are nice information for a birth announcement, but birth weight is especially important for an obstetrician. A large size at delivery has long been associated with an increased risk of injuries to a newborn and its mom. So the better a doctor can predict birth weight, the easier the delivery may be.”\n\nUltrasound is a popular way of doing it. But, aha! You are a Data Scientist (or a “going to be one”(!)). You can amaze people by predicting the birth weight way earlier than ultrasound, right? In this assignment, let’s accomplish this.\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is RMSLE, Root Mean Square Logarithmic Error. \n\nRMSLE is defined as : \n![RMSLE = ](https://drive.google.com/thumbnail?id=12yVAH2wZO17u4KTC1UN8kplaehvZQd-P&amp;sz=w700-h130)\n\nwhere, \n* `n` is total number of samples in the evaluation dataset.\n* `y_i` is the true/known target label for the `i_th` sample\n* y&#770;_i is the predicted target for the input sample xi.\n* `log(.)` is the natural logarithm.\n<hr>\n\n<h2>Submission Format</h2>\n<p><strong>For every author in the dataset</strong>, submission files should contain two columns: ID and BWEIGHT </p>\n<p>The file should contain a header and have the following format:</p>\n<pre>ID,BWEIGHT<br>1,10.1875<br></pre>",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "Rules:\n - It is supposed to be your own work -- so collaboration or formation of team is unexpected.\n - Do not limit yourself to just one model you just finished training. Please remember: Machine learning model development life cycle is incremental. Get the best from all the models -- apply hyperparameter tuning, various algorithms, and ensemble techniques whenever you see fit.\n - Have fun!",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}