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  "fetched_at": "2026-10-20T06:33:08Z",
  "source_url": "https://www.kaggle.com/competitions/gse-524-fall-2021-ames/data",
  "pages": [
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      "name": "data-description",
      "content": "## Files\n\n*   **train_new.csv** - The training set.  There may be missing values.\n*   **test_new.csv** - The test set, which you will make predictions on.  There are no missing values.\n*   **sample_submission_new.csv** - A sample submission file in the correct format\n\n## Columns for Submission\n\n*   `PID` - The property ID\n*   `SalePrice` - The predicted price of the property\n\n\n## IMPORTANT\n\nRemember that your predictions are being assessed based on the **log** RMSE, but that your submissions should have the sale price predictions on the **original** scale.",
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      "name": "Description",
      "content": "## Data\n\nThis dataset consists of information about properties sold in Ames, Iowa.\nYou may have seen this data before, and you may find examples of it being used elsewhere online.  The version in this competition has many of the variables removed, for uniqueness and simplicity.\n\n## Data fields\n\nHere's a brief description of the variables:\n\n* SalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.\n* PID - An identifying number of the property\n* LotFrontage: Linear feet of street connected to property\n* LotArea: Lot size in square feet\n* Street: Type of road access\n* Neighborhood: Physical locations within Ames city limits\n* BldgType: Type of dwelling\n* HouseStyle: Style of dwelling\n* OverallQual: Overall material and finish quality\n* OverallCond: Overall condition rating\n* YearBuilt: Original construction date\n* RoofStyle: Type of roof\n* Heating: Type of heating\n* CentralAir: Central air conditioning\n* Electrical: Electrical system\n* GrLivArea: Above grade (ground) living area square feet\n* FullBath: Full bathrooms above grade\n* HalfBath: Half baths above grade\n* Bedroom AbvGr: Number of bedrooms above basement level\n* TotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\n* Functional: Home functionality rating\n* ScreenPorch: Screen porch area in square feet\n* PoolArea: Pool area in square feet\n* YrSold: Year Sold\n* SaleType: Type of sale\n\n## GOAL\nYour mission today is to use the available variables to predict the sales price of homes.\n\n## Only your first two submissions will be graded!\n\n## Acknowledgments\nThe Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset. ",
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      "name": "Evaluation",
      "content": "## Goal\n\nIt is your job to predict the sales price for each house. For each `PID` in the test set, you must predict the value of the `SalePrice` variable. \n\n## Metric\n\nSubmissions are evaluated on **Root-Mean-Squared-Error (RMSE)** 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| PID | SalePrice |\n| 907135180 | 169000.1 |\n| 903206120  |   187724.1233 |\n\netc.\n\nYou can download an example submission file (`sample_submission.csv`) on the Data page.",
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      "name": "rules",
      "content": "For this exam, you may not:\n\n* Work with any other student in the class on any aspect of the data or modeling process\n\n* Discuss with any other human, whether or not they are a student and whether or not you know them in real life\n\n* Copy from other analyses of this same dataset that may exist online\n\nYou are permitted to use any resource that does not violate the above rules.",
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      "name": "Grading",
      "content": "Your grade on this portion of the Final will be determined by the accuracy score achieved by your submitted predictions.\n\n---\n\n## GRADE TIERS\n\n*Note: these may be subject to change depending on outcomes.  If they are changed, they will only change in the direction of your advantage; i.e., your grade will be higher than expected.*\n\n**0.21 or above:** F\n**0.21 - 0.17:** D range\n**0.17 - 0.16:** C range\n**0.16 - 0.15:** B range\n**0.15 - 0.14:** A range\n**0.14 and below:** A+\n\n## Challenge Points\n\nThe **top 5 scorers** will receive Challenge Points:\n\n1st place: 25\n2nd place: 20\n3rd place: 15\n4th place: 10\n5th place: 5",
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