{
  "fetched_at": "2026-10-19T13:14:10Z",
  "source_url": "https://www.kaggle.com/competitions/california-house-prices/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 about this house\n- ...\n\n",
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      "postTitle": "",
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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 data consist of houses sold in California on 2020, with houses in the test dataset sold after the ones in the training dataset. Also the private leaderboard houses were sold after the ones in the public leaderboard.",
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      "postTitle": "",
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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\n0,223500.0\n1,835000.0\n2,1155000.0\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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      "hasIsPublished": false,
      "postTitle": "",
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}