{
  "fetched_at": "2026-10-20T03:33:44Z",
  "source_url": "https://www.kaggle.com/competitions/2024smarcleks2house-price/data",
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      "name": "data-description",
      "content": "# File descriptions\ntrain.csv - the training set\ntest.csv - the test set\ndata_description.txt - full description of each column, originally prepared by Dean De Cock but lightly edited to match the column names used here\nsample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms\n\n# Data fiel\n\n>  Here's a brief version of what you'll find in the data description file.\n\nSalePrice - the property's sale price in dollars. This is the target variable that you're trying to predict.\nMSSubClass: The building class\nMSZoning: The general zoning classification\nLotFrontage: Linear feet of street connected to property\nLotArea: Lot size in square feet\nStreet: Type of road access\nAlley: Type of alley access\nLotShape: General shape of property\nLandContour: Flatness of the property\nUtilities: Type of utilities available\nLotConfig: Lot configuration\nLandSlope: Slope of property\nNeighborhood: Physical locations within Ames city limits\nCondition1: Proximity to main road or railroad\nCondition2: Proximity to main road or railroad (if a second is present)\nBldgType: Type of dwelling\nHouseStyle: Style of dwelling\nOverallQual: Overall material and finish quality\nOverallCond: Overall condition rating\nYearBuilt: Original construction date\nYearRemodAdd: Remodel date\nRoofStyle: Type of roof\nRoofMatl: Roof material\nExterior1st: Exterior covering on house\nExterior2nd: Exterior covering on house (if more than one material)\nMasVnrType: Masonry veneer type\nMasVnrArea: Masonry veneer area in square feet\nExterQual: Exterior material quality\nExterCond: Present condition of the material on the exterior\nFoundation: Type of foundation\nBsmtQual: Height of the basement\nBsmtCond: General condition of the basement\nBsmtExposure: Walkout or garden level basement walls\nBsmtFinType1: Quality of basement finished area\nBsmtFinSF1: Type 1 finished square feet\nBsmtFinType2: Quality of second finished area (if present)\nBsmtFinSF2: Type 2 finished square feet\nBsmtUnfSF: Unfinished square feet of basement area\nTotalBsmtSF: Total square feet of basement area\nHeating: Type of heating\nHeatingQC: Heating quality and condition\nCentralAir: Central air conditioning\nElectrical: Electrical system\n1stFlrSF: First Floor square feet\n2ndFlrSF: Second floor square feet\nLowQualFinSF: Low quality finished square feet (all floors)\nGrLivArea: Above grade (ground) living area square feet\nBsmtFullBath: Basement full bathrooms\nBsmtHalfBath: Basement half bathrooms\nFullBath: Full bathrooms above grade\nHalfBath: Half baths above grade\nBedroom: Number of bedrooms above basement level\nKitchen: Number of kitchens\nKitchenQual: Kitchen quality\nTotRmsAbvGrd: Total rooms above grade (does not include bathrooms)\nFunctional: Home functionality rating\nFireplaces: Number of fireplaces\nFireplaceQu: Fireplace quality\nGarageType: Garage location\nGarageYrBlt: Year garage was built\nGarageFinish: Interior finish of the garage\nGarageCars: Size of garage in car capacity\nGarageArea: Size of garage in square feet\nGarageQual: Garage quality\nGarageCond: Garage condition\nPavedDrive: Paved driveway\nWoodDeckSF: Wood deck area in square feet\nOpenPorchSF: Open porch area in square feet\nEnclosedPorch: Enclosed porch area in square feet\n3SsnPorch: Three season porch area in square feet\nScreenPorch: Screen porch area in square feet\nPoolArea: Pool area in square feet\nPoolQC: Pool quality\nFence: Fence quality\nMiscFeature: Miscellaneous feature not covered in other categories\nMiscVal: $Value of miscellaneous feature\nMoSold: Month Sold\nYrSold: Year Sold\nSaleType: Type of sale\nSaleCondition: Condition of sale",
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      "name": "Description",
      "content": "fix random seed\n```python\nimport random\nimport os\nimport numpy as np\nseed = 42\nrandom.seed(seed)\nnp.random.seed(seed)\nos.environ[\"PYTHONHASHSEED\"] = str(seed)\n```",
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    {
      "name": "Evaluation",
      "content": "all our schedule is written our github, please check in our site\nhttps://github.com/sejongsmarcle/2024_Spring_Kaggle_Study",
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    {
      "name": "rules",
      "content": "# One account per participant\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\n# No private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\n# Team Mergers\nTeam mergers are allowed and can be performed by the team leader. In order to merge, the combined team must have a total submission count less than or equal to the maximum allowed as of the Team Merger Deadline. The maximum allowed is the number of submissions per day multiplied by the number of days the competition has been running.\n\n# Team Limits\nThe maximum team size is 1.\n\n# Submission Limits\nYou may submit a maximum of 20 entries per day.\n\nYou may select up to 2 final submissions for judging.",
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    {
      "name": "abstract",
      "content": "Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.\n\nWith 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.\n\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. \n\nIt is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable. \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\nwe bring this competition from here\nhttps://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/rules",
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