Human Resources
26 columns
200 rows
8 formats

Employee Attrition Prediction Dataset

This dataset provides detailed HR records for employees, including demographics, job history, satisfaction, performance, and attrition status. It is ideal for building predictive models to identify turnover risks, analyze workforce trends, and inform retention strategies. Rich features enable advanced analytics for HR decision-making and organizational planning.

opened 26 times
last updated 12 Jul 2025
by GoMask

The brief that made it

Predicting employee attrition and turnover risk

Sample rows

preview · 8 of 200 rows · all 26 columns

employee_idstring	employment_statusstring	satisfaction_levelfloat	attritionboolean	education_levelstring	first_namestring	last_namestring	genderstring	date_of_birthdate	marital_statusstring	job_rolestring	departmentstring	manager_idstring	hire_datedate	termination_datedate	tenure_yearsfloat	last_performance_scorefloat	num_promotionsinteger	average_monthly_hoursfloat	salaryfloat	salary_gradeinteger	work_location_citystring	work_location_statestring	work_location_countrystring	has_training_completedboolean	num_projectsinteger
E0001	Active	0.92	false	Master	Riya	Patel	Female	1987-03-11	Married	Finance Manager	Finance	blank	2012-08-01	blank	11.8	4.7	3	168	97000	6	Mumbai	MH	India	true	18
E0002	Active	0.76	false	Bachelor	Carlos	Dominguez	Male	1992-07-25	Single	Data Analyst	Data Science	E0030	2019-05-14	blank	4	3.6	1	146	50000	3	Madrid	MD	Spain	true	6
E0003	Active	0.98	false	Master	Aisha	Okafor	Female	1970-09-02	Widowed	Director of Engineering	Engineering	blank	1994-06-15	blank	29.9	4.9	7	192	205000	8	Lagos	LA	Nigeria	true	29
E0004	Active	0.93	false	Doctorate	Sung-ho	Kim	Male	1980-05-16	Married	Lead Data Scientist	Data Science	E0003	2008-11-23	blank	15.5	4.8	4	178	123000	7	Seoul	11	South Korea	true	21
E0005	Active	0.84	false	Bachelor	Olga	Ivanova	Female	1977-01-29	Divorced	QA Specialist	Quality	E0028	2013-04-03	blank	10.9	4	2	155	69000	4	Moscow	MOW	Russia	true	13
E0006	Active	0.68	false	Bachelor	Lucas	Moreira	Male	1998-12-06	Single	Marketing Associate	Marketing	E0015	2022-01-17	blank	1.9	3.2	0	132	34000	2	Sao Paulo	SP	Brazil	false	3
E0007	Active	0.97	false	Master	Fatima	El-Sayed	Female	1982-10-13	Married	HR Director	Human Resources	blank	2010-03-05	blank	13.9	4.9	5	188	117000	7	Cairo	C	Egypt	true	23
E0008	Retired	0.93	true	High School	David	Smith	Male	1969-08-01	Widowed	Senior Logistics Coordinator	Logistics	E0025	1996-02-12	2023-06-30	27.4	4.2	6	136	64000	5	Houston	TX	USA	true	18
E0009	Active	0.81	false	Bachelor	Emma	Dube	Female	1995-06-20	Single	Customer Support Specialist	Customer Support	E0019	2021-09-01	blank	2.2	3.7	0	144	32000	2	Johannesburg	GT	South Africa	false	4
E0010	Active	0.89	false	Bachelor	Sara	Hassan	Female	1990-11-07	Single	Product Manager	Product	blank	2018-03-20	blank	5.8	4.1	2	159	72000	5	Dubai	DU	United Arab Emirates	true	9
E0011	Active	0.61	false	Associate	Nikhil	Singh	Male	2001-04-15	Single	Intern Developer	Engineering	E0003	2022-06-01	blank	1.7	2.9	0	92	12000	1	Delhi	DL	India	false	2
E0012	Active	0.91	false	Master	Anna	Müller	Female	1984-02-23	Divorced	Quality Lead	Quality	E0028	2009-08-07	blank	14.9	4.5	4	164	84000	5	Frankfurt	HE	Germany	true	15
E0013	Active	0.59	false	High School	Javier	Gomez	Male	1997-12-18	Single	Customer Support Intern	Customer Support	E0019	2023-07-03	blank	0.5	2.5	0	64	9000	1	Buenos Aires	B	Argentina	false	1
E0014	Active	0.87	false	Bachelor	Liam	O'Connor	Male	1978-03-28	Married	Senior Product Designer	Product	E0010	2006-06-10	blank	17.1	4.3	5	170	93000	5	London	ENG	UK	true	19
E0015	Active	0.82	false	Bachelor	Chloe	Martin	Female	1989-05-09	Single	Marketing Manager	Marketing	blank	2015-09-20	blank	8.2	4.1	2	154	71000	4	Paris	IDF	France	true	14
E0016	Active	0.73	false	Bachelor	Kwame	Boateng	Male	1993-10-08	Single	Logistics Coordinator	Logistics	E0025	2018-04-11	blank	5.8	3.3	1	142	41000	3	Accra	AA	Ghana	true	7
E0017	Retired	0.99	true	Master	Isabella	Rossi	Female	1963-02-22	Married	Director of Customer Support	Customer Support	blank	1987-01-12	2022-02-28	35.1	4.8	6	160	82000	6	Rome	RM	Italy	true	26
E0018	Active	0.74	false	Bachelor	Sophie	Dubois	Female	1996-01-14	Single	HR Associate	Human Resources	E0007	2021-04-03	blank	2.6	3.5	0	138	35000	2	Lyon	ARA	France	false	6
E0019	Active	0.79	false	Bachelor	Samuel	Williams	Male	1985-06-23	Divorced	Customer Support Manager	Customer Support	E0017	2012-11-01	blank	11.7	4	3	158	61000	4	Cape Town	WC	South Africa	true	12
E0020	Active	0.77	false	Bachelor	Hiroshi	Tanaka	Male	1991-09-30	Single	Software Engineer	Engineering	E0003	2017-10-10	blank	6.1	3.9	1	147	54000	3	Osaka	27	Japan	true	8

What the 200 rows show

from the 200-row sample

Retired (employment status) stands out: 19 of its 19 rows have attrition = true, against 8 of 181 for the rest.

14%attrition = true
0.80median satisfaction_level
5genders
5marital statuses
11departments
55work location countries

Attrition rate by employment_statusattrition = true

satisfaction_level200 rows, in bands of 0.1

Median 0.80, from 0.32 to 0.99.

education_level198 rows with a value · 2 left blank

Bachelor103
Master42
Other24
High School13
Associate11
Doctorate5

26 columns by typefrom the column list below

string 13
integer 3
float 5
date 3
boolean 2

Columns

26 columns in four groups

blueprint · 26 columns
column	type	description	example
Text 13 columns
`employee_id`	string	Unique identifier for each employeeunique	E0001
`first_name`	string	Employee's first name	Riya
`last_name`	string	Employee's last name	Patel
`gender`	string	Employee's genderMale · Female · Non-binary · Other · Prefer not to say	Female
`marital_status`	string	Employee's marital statusSingle · Married · Divorced · Widowed · Other · optional	Married
`education_level`	string	Highest level of education attained by the employee6 values · optional	Master
`job_role`	string	Employee's job role or title	Finance Manager
`department`	string	Department where the employee works11 departments	Finance
`manager_id`	string	Employee ID of the manager to whom this employee reportsoptional	E0030
`employment_status`	string	Current employment statusActive · Terminated · Resigned · Retired · On Leave	Active
`work_location_city`	string	City where the employee is basedoptional	Mumbai
`work_location_state`	string	State or province of the work locationoptional	MH
`work_location_country`	string	Country of the work locationoptional	India
Numbers 8 columns
`tenure_years`	float	Number of years the employee has worked at the company0 or more	11.8
`satisfaction_level`	float	Employee's job satisfaction level (0.0 to 1.0)0 to 1	0.92
`last_performance_score`	float	Most recent performance evaluation score (0.0 to 5.0)0 to 5	4.7
`num_promotions`	integer	Number of promotions received by the employee0 or more · optional	3
`average_monthly_hours`	float	Average number of hours worked per month0 or more · optional	168
`salary`	float	Current annual salary of the employee in local currency0 or more · optional	97000
`salary_grade`	integer	Salary grade or band assigned to the employee1 or more · optional	6
`num_projects`	integer	Number of projects the employee has participated in0 or more · optional	18
Dates and times 3 columns
`date_of_birth`	date	Employee's date of birth	1987-03-11
`hire_date`	date	Date when the employee was hired	2012-08-01
`termination_date`	date	Date when the employee left the company (null if still employed)optional	2023-06-30
True or false 2 columns
`has_training_completed`	boolean	Indicates if the employee has completed required trainingoptional	true
`attrition`	boolean	Target variable: true if the employee has left the company, false otherwise	false

Use it for

A human resources dashboard
The attrition rate, satisfaction_level by employment_status and a breakdown of education_level. Excel, Power BI or Tableau.
Why do 27 of 200 rows have attrition = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Employees200
E00010.92Active
E00080.93Retired
E00170.99Retired
A software demo
Believable employees with first_name, last_name and gender to fill a screen in front of a buyer.

This dataset200 rows26 columns

Yours10,000 rows26 columnswork_location_city: UK only

blueprint · employee-attrition-prediction-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 26 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Employees with less than 3 months tenure excluded
Salary and performance ratings anonymized
Resignations and terminations differentiated
Department identifiers standardized

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: employee-attrition-prediction-dataset

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A human resources dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Human Resources

What should your data show?