{
    "data_id": "45067",
    "name": "okcupid_stem",
    "exact_name": "okcupid_stem",
    "version": 1,
    "version_label": "1",
    "description": "User profile data for San Francisco OkCupid users published in [Kim, A. Y., & Escobedo-Land, A. (2015). OKCupid data for introductory statistics and data science courses. Journal of Statistics Education, 23(2).]. The curated dataset was downloaded from [https:\/\/github.com\/rudeboybert\/JSE_OkCupid]. The original dataset was created with the use of a python script that pulled the data from public profiles on www.okcupid.com on 06\/30\/2012. It includes people (n = 59946) within a 25 mile radius of San Francisco, who were online in the last year (06\/30\/2011), with at least one profile picture. Permission to use this data was obtained by the author of the original paper from OkCupid president and co-founder Christian Rudder under the condition that the dataset remains public. As target, the variable 'job' was collapsed into three categories: 'stem', 'non_stem', and 'student'. STEM jobs were defined as 'job' %in% c('computer \/ hardware \/ software', 'science \/ tech \/ engineering'). Observations with 'job' %in% c('unemployed', 'retired', 'rather not say') or missing values in 'job' were removed. The factor labels of the variable 'speaks' had to be changed to integers to prevent a bug which would not allow the upload of the variable as a nominal feature. The original dataset also included ten open text variables 'essay0' to 'essay9', which were removed from the dataset uploaded here. The dataset further includes the date\/time variable 'last_online' (ignored by default) which could be used to construct additional features. Using OkCupid data for predicting STEM jobs was inspired by Max Kuhns book 'Feature Engineering and Selection: A Practical Approach for Predictive Models' [https:\/\/bookdown.org\/max\/FES\/].",
    "format": "arff",
    "uploader": "Young Lee",
    "uploader_id": 31892,
    "visibility": "public",
    "creator": "\"Yoontae Hwang, Youngbin Lee, Yongjae Lee\"",
    "contributor": null,
    "date": "2023-01-27 11:22:54",
    "update_comment": null,
    "last_update": "2023-01-27 11:22:54",
    "licence": "Public",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22112025\/dataset",
    "default_target_attribute": "class",
    "row_id_attribute": null,
    "ignore_attribute": null,
    "runs": 0,
    "suggest": {
        "input": [
            "okcupid_stem",
            "User profile data for San Francisco OkCupid users published in [Kim, A. Y., & Escobedo-Land, A. (2015). OKCupid data for introductory statistics and data science courses. Journal of Statistics Education, 23(2).]. The curated dataset was downloaded from [https:\/\/github.com\/rudeboybert\/JSE_OkCupid]. The original dataset was created with the use of a python script that pulled the data from public profiles on www.okcupid.com on 06\/30\/2012. It includes people (n = 59946) within a 25 mile radius of Sa "
        ],
        "weight": 5
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    "qualities": {
        "NumberOfInstances": 26677,
        "NumberOfFeatures": 14,
        "NumberOfClasses": 3,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 2,
        "NumberOfSymbolicFeatures": 11,
        "MajorityClassSize": 19209,
        "MinorityClassPercentage": 10.548412490160063,
        "MinorityClassSize": 2814,
        "NumberOfBinaryFeatures": 1,
        "PercentageOfBinaryFeatures": 7.142857142857142,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": 1,
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        "Dimensionality": 0.0005247966413014956,
        "PercentageOfNumericFeatures": 14.285714285714285,
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    "tags": [
        {
            "uploader": "38960",
            "tag": "Life Science"
        },
        {
            "uploader": "38960",
            "tag": "Machine Learning"
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    "features": [
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            "name": "class",
            "index": "13",
            "type": "string",
            "distinct": "3",
            "missing": "0",
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        },
        {
            "name": "age",
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            "type": "numeric",
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            "missing": "0",
            "min": "18",
            "max": "69",
            "mean": "33",
            "stdev": "10"
        },
        {
            "name": "height",
            "index": "1",
            "type": "numeric",
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            "missing": "0",
            "min": "3",
            "max": "95",
            "mean": "68",
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        },
        {
            "name": "body_type",
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            "type": "nominal",
            "distinct": "12",
            "missing": "0",
            "distr": []
        },
        {
            "name": "drinks",
            "index": "3",
            "type": "nominal",
            "distinct": "6",
            "missing": "0",
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        },
        {
            "name": "drugs",
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            "name": "education",
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        {
            "name": "location",
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            "name": "sex",
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            "name": "smokes",
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    "nr_of_issues": 0,
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