Using Sample Synthetic Data Jobs

Sample Synthetic Data jobs that you can use for working with and testing Protegrity Synthetic Data.

Sample Data Sets

Use the following dataset to test Protegrity Synthetic Data. This dataset is comprehensive and can give you thorough insights into working with Protegrity Synthetic Data.

Adult Dataset: Here is an extract of the dataset, the complete dataset can be found in the adult.csv file in the samples directory.

sex;age;race;marital-status;education;native-country;citizenSince;weight;workclass;occupation;salary-class
Male;39;White;Never-married;Bachelors;United-States;08-01-1971;185.38;State-gov;Adm-clerical;<=50K
Male;50;White;Married-civ-spouse;Bachelors;United-States;19-04-1960;176.32;Self-emp-not-inc;Exec-managerial;<=50K
Male;38;White;Divorced;HS-grad;United-States;07-12-1971;159.13;Private;Handlers-cleaners;<=50K
Male;53;Black;Married-civ-spouse;11th;United-States;22-05-1957;170.45;Private;Handlers-cleaners;<=50K
Female;28;Black;Married-civ-spouse;Bachelors;Cuba;03-02-1982;178.79;Private;Prof-specialty;<=50K
Female;37;White;Married-civ-spouse;Masters;United-States;06-12-1972;161.65;Private;Exec-managerial;<=50K
Female;49;Black;Married-spouse-absent;9th;Jamaica;18-04-1961;162.73;Private;Other-service;<=50K
Male;52;White;Married-civ-spouse;HS-grad;United-States;21-05-1958;171.75;Self-emp-not-inc;Exec-managerial;>50K
Female;31;White;Never-married;Masters;United-States;31-12-1978;164.03;Private;Prof-specialty;>50K
Male;42;White;Married-civ-spouse;Bachelors;United-States;11-02-1968;186.33;Private;Exec-managerial;>50K
Male;37;Black;Married-civ-spouse;Some-college;United-States;06-12-1972;189.49;Private;Exec-managerial;>50K
Male;30;Asian-Pac-Islander;Married-civ-spouse;Bachelors;India;01-02-1980;178.70;State-gov;Prof-specialty;>50K
Female;23;White;Never-married;Bachelors;United-States;08-04-1987;183.22;Private;Adm-clerical;<=50K
Male;32;Black;Never-married;Assoc-acdm;United-States;01-01-1978;156.63;Private;Sales;<=50K
Male;34;Amer-Indian-Eskimo;Married-civ-spouse;7th-8th;Mexico;03-12-1975;173.41;Private;Transport-moving;<=50K
Male;25;White;Never-married;HS-grad;United-States;06-03-1985;170.72;Self-emp-not-inc;Farming-fishing;<=50K
Male;32;White;Never-married;HS-grad;United-States;01-01-1978;174.91;Private;Machine-op-inspct;<=50K
Male;38;White;Married-civ-spouse;11th;United-States;07-12-1971;176.47;Private;Sales;<=50K
Female;43;White;Divorced;Masters;United-States;12-02-1967;179.88;Self-emp-not-inc;Exec-managerial;>50K
Male;40;White;Married-civ-spouse;Doctorate;United-States;09-01-1970;170.80;Private;Prof-specialty;>50K
Female;54;Black;Separated;HS-grad;United-States;23-06-1956;171.61;Private;Other-service;<=50K
Male;35;Black;Married-civ-spouse;9th;United-States;04-12-1974;183.71;Federal-gov;Farming-fishing;<=50K
Male;43;White;Married-civ-spouse;11th;United-States;12-02-1967;158.63;Private;Transport-moving;<=50K
Female;59;White;Divorced;HS-grad;United-States;28-07-1951;181.64;Private;Tech-support;<=50K
Male;56;White;Married-civ-spouse;Bachelors;United-States;25-06-1954;171.80;Local-gov;Tech-support;>50K
Male;19;White;Never-married;HS-grad;United-States;12-05-1991;172.74;Private;Craft-repair;<=50K
Male;39;White;Divorced;HS-grad;United-States;08-01-1971;159.41;Private;Exec-managerial;<=50K
Male;49;White;Married-civ-spouse;HS-grad;United-States;18-04-1961;176.76;Private;Craft-repair;<=50K
Male;23;White;Never-married;Assoc-acdm;United-States;08-04-1987;164.43;Local-gov;Protective-serv;<=50K
Male;20;Black;Never-married;Some-college;United-States;11-05-1990;157.60;Private;Sales;<=50K
Male;45;White;Divorced;Bachelors;United-States;14-03-1965;176.38;Private;Exec-managerial;<=50K
Male;30;White;Married-civ-spouse;Some-college;United-States;01-02-1980;160.60;Federal-gov;Adm-clerical;<=50K
Male;22;Black;Married-civ-spouse;Some-college;United-States;09-04-1988;173.41;State-gov;Other-service;<=50K
Male;48;White;Never-married;11th;Puerto-Rico;17-04-1962;189.50;Private;Machine-op-inspct;<=50K
Male;21;White;Never-married;Some-college;United-States;10-05-1989;162.76;Private;Machine-op-inspct;<=50K
Female;19;White;Married-AF-spouse;HS-grad;United-States;12-05-1991;158.42;Private;Adm-clerical;<=50K
Male;48;White;Married-civ-spouse;Assoc-acdm;United-States;17-04-1962;160.75;Self-emp-not-inc;Prof-specialty;<=50K
Male;31;White;Married-civ-spouse;9th;United-States;31-12-1978;172.10;Private;Machine-op-inspct;<=50K
Male;53;White;Married-civ-spouse;Bachelors;United-States;22-05-1957;189.74;Self-emp-not-inc;Prof-specialty;<=50K
Male;24;White;Married-civ-spouse;Bachelors;United-States;07-04-1986;170.08;Private;Tech-support;<=50K
Female;49;White;Separated;HS-grad;United-States;18-04-1961;173.71;Private;Adm-clerical;<=50K
Male;25;White;Never-married;HS-grad;United-States;06-03-1985;160.52;Private;Handlers-cleaners;<=50K
Male;57;Black;Married-civ-spouse;Bachelors;United-States;26-07-1953;178.12;Federal-gov;Prof-specialty;>50K
Male;53;White;Married-civ-spouse;HS-grad;United-States;22-05-1957;186.11;Private;Machine-op-inspct;<=50K
Female;44;White;Divorced;Masters;United-States;13-02-1966;162.80;Private;Exec-managerial;<=50K
Male;41;White;Married-civ-spouse;Assoc-voc;United-States;10-01-1969;172.39;State-gov;Craft-repair;<=50K
Male;29;White;Never-married;Assoc-voc;United-States;02-02-1981;168.83;Private;Prof-specialty;<=50K
Female;25;Other;Married-civ-spouse;Some-college;United-States;06-03-1985;179.12;Private;Exec-managerial;<=50K
Female;47;White;Married-civ-spouse;Prof-school;Honduras;16-03-1963;163.02;Private;Prof-specialty;>50K
Male;50;White;Divorced;Bachelors;United-States;19-04-1960;172.18;Federal-gov;Exec-managerial;>50K

Bank Customer Chrun Dataset: Here is an extract of the dataset, the complete dataset can be found in the bank_churn.csv file in the samples directory.

RowNumber,CustomerId,Surname,CreditScore,Geography,Gender,Age,Tenure,Balance,NumOfProducts,HasCrCard,IsActiveMember,EstimatedSalary,Exited
1,15634602,Hargrave,619,France,Female,42,2,0,1,1,1,101348.88,1
2,15647311,Hill,608,Spain,Female,41,1,83807.86,1,0,1,112542.58,0
3,15619304,Onio,502,France,Female,42,8,159660.8,3,1,0,113931.57,1
4,15701354,Boni,699,France,Female,39,1,0,2,0,0,93826.63,0
5,15737888,Mitchell,850,Spain,Female,43,2,125510.82,1,1,1,79084.1,0
6,15574012,Chu,645,Spain,Male,44,8,113755.78,2,1,0,149756.71,1
7,15592531,Bartlett,822,France,Male,50,7,0,2,1,1,10062.8,0
8,15656148,Obinna,376,Germany,Female,29,4,115046.74,4,1,0,119346.88,1
9,15792365,He,501,France,Male,44,4,142051.07,2,0,1,74940.5,0
10,15592389,H?,684,France,Male,27,2,134603.88,1,1,1,71725.73,0
11,15767821,Bearce,528,France,Male,31,6,102016.72,2,0,0,80181.12,0
12,15737173,Andrews,497,Spain,Male,24,3,0,2,1,0,76390.01,0
13,15632264,Kay,476,France,Female,34,10,0,2,1,0,26260.98,0
14,15691483,Chin,549,France,Female,25,5,0,2,0,0,190857.79,0
15,15600882,Scott,635,Spain,Female,35,7,0,2,1,1,65951.65,0
16,15643966,Goforth,616,Germany,Male,45,3,143129.41,2,0,1,64327.26,0
17,15737452,Romeo,653,Germany,Male,58,1,132602.88,1,1,0,5097.67,1
18,15788218,Henderson,549,Spain,Female,24,9,0,2,1,1,14406.41,0
19,15661507,Muldrow,587,Spain,Male,45,6,0,1,0,0,158684.81,0
20,15568982,Hao,726,France,Female,24,6,0,2,1,1,54724.03,0
21,15577657,McDonald,732,France,Male,41,8,0,2,1,1,170886.17,0
22,15597945,Dellucci,636,Spain,Female,32,8,0,2,1,0,138555.46,0
23,15699309,Gerasimov,510,Spain,Female,38,4,0,1,1,0,118913.53,1
24,15725737,Mosman,669,France,Male,46,3,0,2,0,1,8487.75,0
25,15625047,Yen,846,France,Female,38,5,0,1,1,1,187616.16,0
26,15738191,Maclean,577,France,Male,25,3,0,2,0,1,124508.29,0
27,15736816,Young,756,Germany,Male,36,2,136815.64,1,1,1,170041.95,0
28,15700772,Nebechi,571,France,Male,44,9,0,2,0,0,38433.35,0
29,15728693,McWilliams,574,Germany,Female,43,3,141349.43,1,1,1,100187.43,0
30,15656300,Lucciano,411,France,Male,29,0,59697.17,2,1,1,53483.21,0
31,15589475,Azikiwe,591,Spain,Female,39,3,0,3,1,0,140469.38,1
32,15706552,Odinakachukwu,533,France,Male,36,7,85311.7,1,0,1,156731.91,0
33,15750181,Sanderson,553,Germany,Male,41,9,110112.54,2,0,0,81898.81,0
34,15659428,Maggard,520,Spain,Female,42,6,0,2,1,1,34410.55,0
35,15732963,Clements,722,Spain,Female,29,9,0,2,1,1,142033.07,0
36,15794171,Lombardo,475,France,Female,45,0,134264.04,1,1,0,27822.99,1
37,15788448,Watson,490,Spain,Male,31,3,145260.23,1,0,1,114066.77,0
38,15729599,Lorenzo,804,Spain,Male,33,7,76548.6,1,0,1,98453.45,0
39,15717426,Armstrong,850,France,Male,36,7,0,1,1,1,40812.9,0
40,15585768,Cameron,582,Germany,Male,41,6,70349.48,2,0,1,178074.04,0
41,15619360,Hsiao,472,Spain,Male,40,4,0,1,1,0,70154.22,0
42,15738148,Clarke,465,France,Female,51,8,122522.32,1,0,0,181297.65,1
43,15687946,Osborne,556,France,Female,61,2,117419.35,1,1,1,94153.83,0
44,15755196,Lavine,834,France,Female,49,2,131394.56,1,0,0,194365.76,1
45,15684171,Bianchi,660,Spain,Female,61,5,155931.11,1,1,1,158338.39,0
46,15754849,Tyler,776,Germany,Female,32,4,109421.13,2,1,1,126517.46,0
47,15602280,Martin,829,Germany,Female,27,9,112045.67,1,1,1,119708.21,1
48,15771573,Okagbue,637,Germany,Female,39,9,137843.8,1,1,1,117622.8,1
49,15766205,Yin,550,Germany,Male,38,2,103391.38,1,0,1,90878.13,0

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Last modified : November 10, 2025