{
  "fetched_at": "2026-10-19T13:56:53Z",
  "source_url": "https://www.kaggle.com/competitions/d2l-house-price/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## File descriptions\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\n## Data fields\n\n- Id - the house ID\n- Sold Price - the price this house was sold \n- Summary - seller summary\n- ...\n\n",
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    {
      "name": "Description",
      "content": "The task is to predict house sale prices based on the house information, such as # of bedrooms, living areas, locations, near-by schools, and the seller summary. The training dataset consists of 40K houses sold in the [bay area](https://en.wikipedia.org/wiki/San_Francisco_Bay_Area) before 05/01/2020.  The test dataset has 23K houses sold after, which is divided into two parts for the public leaderboard and private leaderboard, respectively. The private leaderboard houses were sold after the ones in the public leaderboard.",
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    {
      "name": "Evaluation",
      "content": "## Metric\n\nSubmissions are evaluated on [Root-Mean-Squared-Error (RMSE)](https://en.wikipedia.org/wiki/Root-mean-square_deviation) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.)\n\n## Submission File Format\n\nThe file should contain a header and have the following format:\n\n```\nId,Sold Price\n2076037805,223500.0\n153558840,835000.0\n19511211,1155000.0\n69302785,705000.0\n82955526,1005000.0\n```",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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