{ "data_id": "44002", "name": "house_sales", "exact_name": "house_sales", "version": 4, "version_label": null, "description": "Dataset used in the tabular data benchmark https:\/\/github.com\/LeoGrin\/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: \n \nDate converted to year\/mo\/day numerics.This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015.\n\nIt contains 19 house features plus the price and the id columns, along with 21613 observations.\nIt's a great dataset for evaluating simple regression models.", "format": "arff", "uploader": "Leo Grin", "uploader_id": 26324, "visibility": "public", "creator": "\"https:\/\/www.kaggle.com\/harlfoxem\/\"", "contributor": "\"Leo Grin\"", "date": "2022-06-16 20:53:54", "update_comment": null, "last_update": "2022-06-16 20:53:54", "licence": "CC0 Public Domain", "status": "active", "error_message": null, "url": "https:\/\/old.openml.org\/data\/download\/22103090\/dataset", "default_target_attribute": "price", "row_id_attribute": null, "ignore_attribute": null, "runs": 0, "suggest": { "input": [ "house_sales", "Dataset used in the tabular data benchmark https:\/\/github.com\/LeoGrin\/tabular-benchmark, transformed in the same way. This dataset belongs to the \"classification on numerical features\" benchmark. Original description: Date converted to year\/mo\/day numerics.This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It contains 19 house features plus the price and the id columns, along with 21613 observations. It's a great " ], "weight": 5 }, "qualities": { "NumberOfInstances": 21613, "NumberOfFeatures": 16, "NumberOfClasses": 0, "NumberOfMissingValues": 0, "NumberOfInstancesWithMissingValues": 0, "NumberOfNumericFeatures": 15, "NumberOfSymbolicFeatures": 1, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 0, "AutoCorrelation": 0.42179305046726, "PercentageOfMissingValues": 0, "Dimensionality": 0.0007402951927080924, "PercentageOfNumericFeatures": 93.75, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 6.25, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0 }, "tags": [ { "uploader": "38960", "tag": "Machine Learning" }, { "uploader": "38960", "tag": "Statistics" } ], "features": [ { "name": "price", "index": "15", "type": "numeric", "distinct": "4028", "missing": "0", "target": "1", "min": "11", "max": "16", "mean": "13", "stdev": "1" }, { "name": "bedrooms", "index": "0", "type": "numeric", "distinct": "13", "missing": "0", "min": "0", "max": "33", "mean": "3", "stdev": "1" }, { "name": "bathrooms", "index": "1", "type": "numeric", "distinct": "30", "missing": "0", "min": "0", "max": "8", "mean": "2", "stdev": "1" }, { "name": "sqft_living", "index": "2", "type": "numeric", "distinct": "1038", "missing": "0", "min": "290", "max": "13540", "mean": "2080", "stdev": "918" }, { "name": "sqft_lot", "index": "3", "type": "numeric", "distinct": "9782", "missing": "0", "min": "520", "max": "1651359", "mean": "15107", "stdev": "41421" }, { "name": "grade", "index": "4", "type": "numeric", "distinct": "12", "missing": "0", "min": "1", "max": "13", "mean": "8", "stdev": "1" }, { "name": "sqft_above", "index": "5", "type": "numeric", "distinct": "946", "missing": "0", "min": "290", "max": "9410", "mean": "1788", "stdev": "828" }, { "name": "sqft_basement", "index": "6", "type": "numeric", "distinct": "306", "missing": "0", "min": "0", "max": "4820", "mean": "292", "stdev": "443" }, { "name": "yr_built", "index": "7", "type": "numeric", "distinct": "116", "missing": "0", "min": "1900", "max": "2015", "mean": "1971", "stdev": "29" }, { "name": "yr_renovated", "index": "8", "type": "numeric", "distinct": "70", "missing": "0", "min": "0", "max": "2015", "mean": "84", "stdev": "402" }, { "name": "lat", "index": "9", "type": "numeric", "distinct": "5034", "missing": "0", "min": "47", "max": "48", "mean": "48", "stdev": "0" }, { "name": "long", "index": "10", "type": "numeric", "distinct": "752", "missing": "0", "min": "-123", "max": "0", "mean": "-122", "stdev": "0" }, { "name": "sqft_living15", "index": "11", "type": "numeric", "distinct": "777", "missing": "0", "min": "399", "max": "6210", "mean": "1987", "stdev": "685" }, { "name": "sqft_lot15", "index": "12", "type": "numeric", "distinct": "8689", "missing": "0", "min": "651", "max": "871200", "mean": "12768", "stdev": "27304" }, { "name": "date_month", "index": "13", "type": "nominal", "distinct": "12", "missing": "0", "distr": [] }, { "name": "date_day", "index": "14", "type": "numeric", "distinct": "31", "missing": "0", "min": "1", "max": "31", "mean": "16", "stdev": "9" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }