Human Resources
36 columns
150 rows
8 formats

Employee Attrition Prediction

This dataset provides comprehensive historical HR records, including employee demographics, job details, compensation, satisfaction metrics, and attrition status. It is ideal for building predictive models to forecast employee turnover and for analyzing factors influencing retention. The rich feature set supports advanced HR analytics, workforce planning, and targeted retention strategies.

opened 17 times
last updated 12 Jul 2025
by GoMask

The brief that made it

Predicting employee attrition and turnover risk

Sample rows

preview · 8 of 150 rows · all 36 columns

employee_idstring	marital_statusstring	num_dependentsinteger	is_activeboolean	genderstring	first_namestring	last_namestring	date_of_birthdate	hire_datedate	termination_datedate	departmentstring	job_rolestring	manager_idstring	education_levelstring	salaryfloat	bonusfloat	job_levelinteger	years_at_companyfloat	years_in_current_rolefloat	years_since_last_promotionfloat	years_with_current_managerfloat	performance_ratinginteger	overtimeboolean	work_life_balanceinteger	job_satisfactioninteger	environment_satisfactioninteger	training_times_last_yearinteger	num_companies_workedinteger	distance_from_homefloat	business_travelstring	attritionboolean	address_streetstring	address_citystring	address_statestring	address_postal_codestring	address_countrystring
EMP-0001	Single	0	true	Female	Sophia	Martinez	1989-06-08	2015-08-15	blank	IT	Software Engineer	EMP-0003	Bachelor	87000	5000	2	8.8	3.5	2.2	2	4	true	2	3	3	2	1	4.1	Travel_Rarely	false	782 Oakridge Dr	Boston	MA	02118	USA
EMP-0002	Married	2	true	Male	Jackson	Lee	1972-04-21	2001-09-01	blank	Finance	Finance Manager	blank	Master	127000	15000	4	22.2	10.4	2.7	6.8	5	false	3	4	4	1	2	8.5	Travel_Rarely	false	390 Pinecrest Ave	Nashville	TN	37211	USA
EMP-0003	Married	3	true	Female	Ava	Patel	1981-01-13	2010-04-10	blank	IT	IT Manager	blank	Master	112000	12000	4	13.9	7.6	1.9	5.2	4	false	3	4	4	1	2	12.7	Travel_Rarely	false	1507 Willow St	Austin	TX	78702	USA
EMP-0004	Single	0	false	Male	Mason	Young	1996-10-07	2021-07-12	2023-06-18	Sales	Sales Representative	EMP-0007	Bachelor	49000	1200	1	1.9	1.9	1.9	1.7	3	true	2	2	2	3	0	1.8	Travel_Frequently	true	89 Harbor Dr	Tampa	FL	33602	USA
EMP-0005	Single	0	true	Female	Isabella	Nguyen	1992-03-19	2017-11-03	blank	Engineering	Quality Analyst	EMP-0010	Bachelor	79000	4000	2	5.6	3.6	2.8	2.1	4	true	3	3	3	2	1	7.2	Travel_Rarely	false	512 Summit Pl	Denver	CO	80203	USA
EMP-0006	Married	2	true	Male	Aiden	Robinson	1986-12-14	2014-02-24	blank	IT	Systems Administrator	EMP-0003	Bachelor	92000	6000	3	9.9	5.9	3.1	3	3	true	2	3	2	1	2	2.2	Travel_Rarely	false	671 Birchwood Ave	Chicago	IL	60616	USA
EMP-0007	Married	3	true	Male	Logan	White	1982-07-02	2009-10-12	blank	Sales	Sales Manager	blank	Bachelor	117000	17500	4	14.1	7.2	1.2	4	4	false	3	4	3	1	2	12.5	Travel_Frequently	false	213 Cedar Ct	Charlotte	NC	28202	USA
EMP-0008	Married	2	true	Male	Lucas	Perez	1978-09-21	2012-01-25	blank	Operations	Operations Analyst	EMP-0012	Bachelor	84000	3500	3	11.2	6.8	3.6	3.2	4	false	3	3	3	2	2	7.9	Non-Travel	false	490 King St	Seattle	WA	98101	USA
EMP-0009	Single	0	true	Female	Mia	Carter	1998-02-23	2022-03-15	blank	Marketing	Marketing Associate	EMP-0015	Bachelor	54000	1500	1	1.8	1.8	1.8	1.8	3	false	3	3	2	2	0	3.5	Travel_Rarely	false	347 Main St	Boston	MA	02116	USA
EMP-0010	Married	2	true	Female	Ella	Thomas	1975-11-11	2005-06-13	blank	Engineering	Engineering Manager	blank	Master	153000	22000	5	18.7	8.2	2.5	6	5	false	4	4	4	1	2	18.3	Travel_Frequently	false	659 Cypress Rd	Austin	TX	78701	USA
EMP-0011	Single	0	true	Male	Liam	Johnson	1994-07-29	2020-09-28	blank	Development	Junior Developer	EMP-0017	Associate	37000	0	1	3.1	3.1	3.1	2.5	3	true	2	2	2	4	1	0.7	Non-Travel	false	889 Walnut St	Raleigh	NC	27601	USA
EMP-0012	Married	2	true	Male	Benjamin	Wright	1984-12-18	2011-10-04	blank	Operations	Operations Manager	blank	Master	118500	9000	4	11.7	5.4	2.1	3	4	false	3	3	3	1	2	9.4	Travel_Rarely	false	231 Lakeview Dr	Orlando	FL	32801	USA
EMP-0013	Single	0	false	Female	Zoe	Murphy	1997-03-10	2021-02-20	2022-12-07	HR	HR Coordinator	EMP-0018	Bachelor	45000	0	1	1.8	1.8	1.8	1.2	2	false	2	2	2	2	0	4.6	Non-Travel	true	322 Summer St	Phoenix	AZ	85004	USA
EMP-0014	Married	1	true	Male	Ethan	Stewart	1988-12-28	2016-06-01	blank	Development	Software Developer	EMP-0017	Bachelor	85000	3000	2	7.1	4.4	3.2	2.1	3	true	2	3	3	3	1	6.3	Travel_Rarely	false	763 Aspen Ct	Denver	CO	80211	USA
EMP-0015	Married	2	true	Female	Olivia	Bennett	1985-05-24	2018-01-26	blank	Marketing	Marketing Manager	blank	Master	101000	11000	3	5.4	3.2	1.7	2.5	4	false	3	4	4	1	2	13.3	Travel_Rarely	false	315 Spruce St	San Francisco	CA	94103	USA
EMP-0016	Single	0	false	Male	Matthew	Ramirez	1993-11-17	2019-10-21	2023-04-01	IT	Help Desk Technician	EMP-0003	Associate	43000	0	1	3.5	3.5	3.5	2.5	2	true	2	2	2	3	1	1.2	Non-Travel	true	201 Ridge Ave	Dallas	TX	75201	USA
EMP-0017	Married	2	true	Male	Alexander	King	1980-08-04	2008-02-12	blank	Development	Development Manager	blank	Master	118000	11500	4	15.1	5.7	2	3.9	4	false	3	4	4	1	2	9.8	Travel_Rarely	false	402 Highland St	Austin	TX	78705	USA
EMP-0018	Married	2	true	Female	Charlotte	Mitchell	1973-02-02	2002-07-17	blank	HR	HR Manager	blank	Master	113500	9000	4	21.7	7.8	2.5	6.7	5	false	4	4	4	1	2	6.2	Non-Travel	false	121 Elm St	Madison	WI	53703	USA
EMP-0019	Single	0	false	Other	Harper	Kim	1995-09-14	2020-01-16	2022-07-22	Engineering	Test Engineer	EMP-0010	Bachelor	69000	3000	2	2.5	2.5	2.5	2	3	true	2	2	2	2	1	2.7	Travel_Rarely	true	801 Grove St	Boulder	CO	80302	USA
EMP-0020	Married	1	true	Female	Grace	Hernandez	1987-06-30	2016-05-18	blank	Finance	Accountant	EMP-0002	Bachelor	81000	3500	2	7.8	4.2	2.3	3.5	4	false	3	3	3	2	1	5.7	Travel_Rarely	false	979 Ridge St	Boston	MA	02115	USA

What the 150 rows show

from the 150-row sample

Single (marital status) stands out: mean num_dependents is 0.0, against 1.9 for the rest.

82%is_active = true
0median num_dependents
3business travels
5education levels
9departments
29address states

Mean num_dependents by marital_status148 rows

num_dependents148 rows, in bands of 0.5

Median 0, from 0 to 3.

gender148 rows with a value · 2 left blank

Male72
Female67
Other6
Prefer not to say3

36 columns by typefrom the column list below

string 15
integer 8
float 7
date 3
boolean 3

Columns

36 columns in four groups

blueprint · 36 columns
column	type	description	example
Text 15 columns
`employee_id`	string	Unique identifier for each employeeunique	EMP-0001
`first_name`	string	Employee's first name	Sophia
`last_name`	string	Employee's last name	Martinez
`gender`	string	Employee's genderMale · Female · Other · Prefer not to say · optional	Female
`department`	string	Department where the employee works9 departments	IT
`job_role`	string	Employee's job title or role	Software Engineer
`manager_id`	string	Employee ID of the manageroptional	EMP-0003
`education_level`	string	Highest education level attained6 values · optional	Bachelor
`marital_status`	string	Employee's marital statusSingle · Married · Divorced · Widowed · Other · optional	Single
`business_travel`	string	Frequency of business travelNon-Travel · Travel_Rarely · Travel_Frequently · optional	Travel_Rarely
`address_street`	string	Employee's residential street addressoptional	782 Oakridge Dr
`address_city`	string	Employee's residential cityoptional	Boston
`address_state`	string	Employee's residential state or provinceoptional	MA
`address_postal_code`	string	Employee's residential postal codeoptional	02118
`address_country`	string	Employee's residential countryoptional	USA
Numbers 15 columns
`num_dependents`	integer	Number of dependents the employee has0 or more · optional	0
`salary`	float	Current annual salary of the employee in USD0 or more	87000
`bonus`	float	Annual bonus received by the employee in USD0 or more · optional	5000
`job_level`	integer	Job level or grade within the organization1 or more · optional	2
`years_at_company`	float	Total number of years the employee has worked at the company0 or more	8.8
`years_in_current_role`	float	Number of years the employee has been in their current role0 or more · optional	3.5
`years_since_last_promotion`	float	Years since the employee's last promotion0 or more · optional	2.2
`years_with_current_manager`	float	Years the employee has worked with their current manager0 or more · optional	2
`performance_rating`	integer	Most recent performance rating (e.g., 1-5 scale)1 to 5 · optional	4
`work_life_balance`	integer	Employee's work-life balance rating (e.g., 1-4 scale)1 to 4 · optional	2
`job_satisfaction`	integer	Employee's job satisfaction rating (e.g., 1-4 scale)1 to 4 · optional	3
`environment_satisfaction`	integer	Employee's satisfaction with work environment (e.g., 1-4 scale)1 to 4 · optional	3
`training_times_last_year`	integer	Number of training sessions attended in the last year0 or more · optional	2
`num_companies_worked`	integer	Number of companies the employee has previously worked for0 or more · optional	1
`distance_from_home`	float	Distance from employee's home to workplace in miles0 or more · optional	4.1
Dates and times 3 columns
`date_of_birth`	date	Employee's date of birthoptional	1989-06-08
`hire_date`	date	Date the employee was hired	2015-08-15
`termination_date`	date	Date the employee left the organization, null if still employedoptional	2023-06-18
True or false 3 columns
`is_active`	boolean	Indicates if the employee is currently employed	true
`overtime`	boolean	Indicates if the employee regularly works overtimeoptional	true
`attrition`	boolean	Indicates if the employee has left the company (target variable)	false

Use it for

A human resources dashboard
The is_active rate, num_dependents by marital_status and a breakdown of gender. Excel, Power BI or Tableau.
Why do the 77 Single rows have a mean num_dependents of 0.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Employees150
EMP-00010Single
EMP-00022Married
EMP-00040Single
A software demo
Believable employees with first_name, last_name and gender to fill a screen in front of a buyer.

This dataset150 rows36 columns

Yours10,000 rows36 columnsaddress_street: UK only

blueprint · employee-attrition-prediction

Behind this dataset

Same schema. As many rows as you need.

These 150 rows came out of a blueprint — 36 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 tenure <6 months excluded
Exit interviews required for all departures
Resignation reasons standardized
Active employees must have last appraisal date

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

What should your data show?

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Sample rows

What the 150 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?