Data
okcupid-stem

okcupid-stem

active ARFF Publicly available Visibility: public Uploaded 19-11-2020 by Marcos de Paula Bueno
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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 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/].

20 features

job (target)nominal3 unique values
0 missing
agenumeric53 unique values
0 missing
body_typenominal12 unique values
3905 missing
dietnominal18 unique values
19382 missing
drinksnominal6 unique values
1552 missing
drugsnominal3 unique values
11622 missing
educationnominal32 unique values
3486 missing
ethnicitynominal208 unique values
3989 missing
heightnumeric57 unique values
1 missing
incomenominal12 unique values
39886 missing
locationnominal184 unique values
0 missing
offspringnominal15 unique values
28914 missing
orientationnominal3 unique values
0 missing
petsnominal15 unique values
14938 missing
religionnominal45 unique values
15126 missing
sexnominal2 unique values
0 missing
signnominal48 unique values
7700 missing
smokesnominal5 unique values
3572 missing
speaksnominal7019 unique values
34 missing
statusnominal5 unique values
0 missing

19 properties

50789
Number of instances (rows) of the dataset.
20
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
154107
Number of missing values in the dataset.
48622
Number of instances with at least one value missing.
2
Number of numeric attributes.
18
Number of nominal attributes.
90
Percentage of nominal attributes.
71.57
Percentage of instances belonging to the most frequent class.
36350
Number of instances belonging to the most frequent class.
9.61
Percentage of instances belonging to the least frequent class.
4882
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
5
Percentage of binary attributes.
95.73
Percentage of instances having missing values.
15.17
Percentage of missing values.
0.57
Average class difference between consecutive instances.
10
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: job
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