DEVELOPMENT...      {
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    "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": " Smith",
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    "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",
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    "url": "https:\/\/api.openml.org\/data\/download\/22112025\/dataset",
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