IBM Employee Dataset

Predict Employee Attrition

@kaggle.rohitsahoo_employee

Table List

2 tables(97.79 KB)

Test

@kaggle.rohitsahoo_employee.test

38.61 KB
412 rows
34 columns

age Age	businesstravel BusinessTravel	dailyrate DailyRate	department Department	distancefromhome DistanceFromHome	education Education	educationfield EducationField	employeecount EmployeeCount	employeenumber EmployeeNumber	environmentsatisfaction EnvironmentSatisfaction	gender Gender	hourlyrate HourlyRate	jobinvolvement JobInvolvement	joblevel JobLevel	jobrole JobRole	jobsatisfaction JobSatisfaction	maritalstatus MaritalStatus	monthlyincome MonthlyIncome	monthlyrate MonthlyRate	numcompaniesworked NumCompaniesWorked	over18 Over18	overtime OverTime	percentsalaryhike PercentSalaryHike	performancerating PerformanceRating	relationshipsatisfaction RelationshipSatisfaction	standardhours StandardHours	stockoptionlevel StockOptionLevel	totalworkingyears TotalWorkingYears	trainingtimeslastyear TrainingTimesLastYear	worklifebalance WorkLifeBalance	yearsatcompany YearsAtCompany	yearsincurrentrole YearsInCurrentRole	yearssincelastpromotion YearsSinceLastPromotion	yearswithcurrmanager YearsWithCurrManager
34	Travel_Rarely	790	Sales	24	4	Medical	1	1489	1	Female	40	2	2	Sales Executive	2	Single	4599	7815		Y	Yes	23	4	3	80		16	2	4	15	9	10	10
35	Travel_Rarely	660	Sales	7	1	Life Sciences	1	1492	4	Male	76	3	1	Sales Representative	3	Married	2404	16192	1	Y	No	13	3	1	80	1	1	3	3	1
24	Travel_Frequently	381	Research & Development	9	3	Medical	1	1494	2	Male	89	3	1	Laboratory Technician	1	Single	3172	16998	2	Y	Yes	11	3	3	80		4	2	2
24	Non-Travel	830	Sales	13	2	Life Sciences	1	1495	4	Female	78	3	1	Sales Representative	2	Married	2033	7103	1	Y	No	13	3	3	80	1	1	2	3	1
44	Travel_Frequently	1193	Research & Development	2	1	Medical	1	1496	2	Male	86	3	3	Manufacturing Director	3	Single	10209	19719	5	Y	Yes	18	3	2	80		16	2	2	2	2	2	2
29	Travel_Rarely	1246	Sales	19	3	Life Sciences	1	1497	3	Male	77	2	2	Sales Executive	3	Divorced	8620	23757	1	Y	No	14	3	3	80	2	10	3	3	10	7		4
30	Travel_Rarely	330	Human Resources	1	3	Life Sciences	1	1499	3	Male	46	3	1	Human Resources	3	Divorced	2064	15428		Y	No	21	4	1	80	1	6	3	4	5	3	1	3
55	Travel_Rarely	1229	Research & Development	4	4	Life Sciences	1	1501	4	Male	30	3	2	Healthcare Representative	3	Married	4035	16143		Y	Yes	16	3	2	80		4	2	3	3	2	1	2
33	Travel_Rarely	1099	Research & Development	4	4	Medical	1	1502	1	Female	82	2	1	Laboratory Technician	2	Married	3838	8192	8	Y	No	11	3	4	80		8	5	3	5	4		2
47	Travel_Rarely	571	Sales	14	3	Medical	1	1503	3	Female	78	3	2	Sales Executive	3	Married	4591	24200	3	Y	Yes	17	3	3	80	1	11	4	2	5	4	1	2


CREATE TABLE test (
  "age" BIGINT,
  "businesstravel" VARCHAR,
  "dailyrate" BIGINT,
  "department" VARCHAR,
  "distancefromhome" BIGINT,
  "education" BIGINT,
  "educationfield" VARCHAR,
  "employeecount" BIGINT,
  "employeenumber" BIGINT,
  "environmentsatisfaction" BIGINT,
  "gender" VARCHAR,
  "hourlyrate" BIGINT,
  "jobinvolvement" BIGINT,
  "joblevel" BIGINT,
  "jobrole" VARCHAR,
  "jobsatisfaction" BIGINT,
  "maritalstatus" VARCHAR,
  "monthlyincome" BIGINT,
  "monthlyrate" BIGINT,
  "numcompaniesworked" BIGINT,
  "over18" VARCHAR,
  "overtime" VARCHAR,
  "percentsalaryhike" BIGINT,
  "performancerating" BIGINT,
  "relationshipsatisfaction" BIGINT,
  "standardhours" BIGINT,
  "stockoptionlevel" BIGINT,
  "totalworkingyears" BIGINT,
  "trainingtimeslastyear" BIGINT,
  "worklifebalance" BIGINT,
  "yearsatcompany" BIGINT,
  "yearsincurrentrole" BIGINT,
  "yearssincelastpromotion" BIGINT,
  "yearswithcurrmanager" BIGINT
);