{
  "fetched_at": "2026-10-19T14:39:48Z",
  "source_url": "https://www.kaggle.com/competitions/2022-pa-hackathon-adv-competition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Description of datasets\n\nThe datasets describes California house price data. The target column is the price as which each house was sold. Features regarding each house is provided including fields such as:  \n* Year built  \n* Square footage  \n* High school score  \n* Type of flooring, heating cooling etc.  \n\n## Files provided\n\nThree files have been provided in csv format:\n\n* `train.csv` - the training dataset  \n* `test.csv` - predict the sold price for this dataset for submission\n* `sample_submission.csv` - a sample submission in the correct format\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 data consist of houses sold in California and Arizona in 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 training dataset.",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is root mean squared error (RMSE).\n\nField required in the submission are:  \n* id  \n* Sold Price  ",
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    {
      "name": "rules",
      "content": "To Qualify as winner, you will be required to disclose code and solution approach to organizing committee, and be able to replicate your solutions. You will be also invited to present your solution to all other participants at the upcoming 2022 joint PA seminar hosted by the CAS, CIA and SOA\n\nYou are allowed to find external data that could potentially augment the existing dataset, however using the actual sold prices of these homes - should you be able to find them, would be prohibited and disqualify your submissions.\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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