{
    "data_id": "43440",
    "name": "Forest-Fire-Area",
    "exact_name": "Forest-Fire-Area",
    "version": 1,
    "version_label": "v1.0",
    "description": "Content\nThe dataset contains 517 fires from the Montesinho natural park in Portugal. For each incident weekday, month, coordinates, and the burnt area are recorded, as well as several meteorological data such as rain, temperature, humidity, and wind. The workflow reads the data and trains a regression model based on the spatial, temporal, and weather variables.\nAcknowledgements\nAll credit for this dataset goes to P. Cortez and A. Morais.\nP. Cortez and A. Morais. A Data Mining Approach to Predict Forest Fires using Meteorological Data. In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimaraes, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9.\nBurning Area Prediction",
    "format": "arff",
    "uploader": "Onur Yildirim",
    "uploader_id": 30126,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-03-23 13:21:07",
    "update_comment": null,
    "last_update": "2022-03-23 13:21:07",
    "licence": "CC0: Public Domain",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/www.openml.org\/data\/download\/22102265\/dataset",
    "default_target_attribute": "area",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "Forest-Fire-Area",
            "Content The dataset contains 517 fires from the Montesinho natural park in Portugal. For each incident weekday, month, coordinates, and the burnt area are recorded, as well as several meteorological data such as rain, temperature, humidity, and wind. The workflow reads the data and trains a regression model based on the spatial, temporal, and weather variables. Acknowledgements All credit for this dataset goes to P. Cortez and A. Morais. P. Cortez and A. Morais. A Data Mining Approach to Predict "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 517,
        "NumberOfFeatures": 13,
        "NumberOfClasses": 0,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 11,
        "NumberOfSymbolicFeatures": 0,
        "Dimensionality": 0.025145067698259187,
        "PercentageOfNumericFeatures": 84.61538461538461,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": -13.814806201550388,
        "PercentageOfMissingValues": 0
    },
    "tags": [
        {
            "uploader": "38960",
            "tag": "Computer Systems"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
        }
    ],
    "features": [
        {
            "name": "area",
            "index": "12",
            "type": "numeric",
            "distinct": "251",
            "missing": "0",
            "target": "1",
            "min": "0",
            "max": "1091",
            "mean": "13",
            "stdev": "64"
        },
        {
            "name": "X",
            "index": "0",
            "type": "numeric",
            "distinct": "9",
            "missing": "0",
            "min": "1",
            "max": "9",
            "mean": "5",
            "stdev": "2"
        },
        {
            "name": "Y",
            "index": "1",
            "type": "numeric",
            "distinct": "7",
            "missing": "0",
            "min": "2",
            "max": "9",
            "mean": "4",
            "stdev": "1"
        },
        {
            "name": "month",
            "index": "2",
            "type": "string",
            "distinct": "12",
            "missing": "0"
        },
        {
            "name": "day",
            "index": "3",
            "type": "string",
            "distinct": "7",
            "missing": "0"
        },
        {
            "name": "FFMC",
            "index": "4",
            "type": "numeric",
            "distinct": "106",
            "missing": "0",
            "min": "19",
            "max": "96",
            "mean": "91",
            "stdev": "6"
        },
        {
            "name": "DMC",
            "index": "5",
            "type": "numeric",
            "distinct": "215",
            "missing": "0",
            "min": "1",
            "max": "291",
            "mean": "111",
            "stdev": "64"
        },
        {
            "name": "DC",
            "index": "6",
            "type": "numeric",
            "distinct": "219",
            "missing": "0",
            "min": "8",
            "max": "861",
            "mean": "548",
            "stdev": "248"
        },
        {
            "name": "ISI",
            "index": "7",
            "type": "numeric",
            "distinct": "119",
            "missing": "0",
            "min": "0",
            "max": "56",
            "mean": "9",
            "stdev": "5"
        },
        {
            "name": "temp",
            "index": "8",
            "type": "numeric",
            "distinct": "192",
            "missing": "0",
            "min": "2",
            "max": "33",
            "mean": "19",
            "stdev": "6"
        },
        {
            "name": "RH",
            "index": "9",
            "type": "numeric",
            "distinct": "75",
            "missing": "0",
            "min": "15",
            "max": "100",
            "mean": "44",
            "stdev": "16"
        },
        {
            "name": "wind",
            "index": "10",
            "type": "numeric",
            "distinct": "21",
            "missing": "0",
            "min": "0",
            "max": "9",
            "mean": "4",
            "stdev": "2"
        },
        {
            "name": "rain",
            "index": "11",
            "type": "numeric",
            "distinct": "7",
            "missing": "0",
            "min": "0",
            "max": "6",
            "mean": "0",
            "stdev": "0"
        }
    ],
    "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
}