Analiza danych - projekt zespołowy - HR
Odejścia pracowników
Odejścia pracowników prowadzą do znaczących kosztów dla firmy poprzez zakłócenia w działalności, konieczność rekrutacji i szkolenia nowych pracowników. Dlatego też, dział HR stara się zrozumieć czynniki, które wpływają na odejścia pracowników, aby skutecznie je minimalizować. Problem odejść pracowników jest złożonym wyzwaniem, które wymagazaawansowanego podejścia w zarządzaniu zasobami ludzkimi. Celem projektu jest przewidywanie, którzy pracownicy mogązdecydować się na odejście z organizacji, aby odpowiednio wcześniej podjąć działania prewencyjne.
Analityka w tym przypadku odgrywa kluczową rolę w interpretacji danych organizacyjnych. Dzięki analizie danych można dostrzec ukryte wzorce i trendy związane z pracownikami, umożliwiając podjęcie odpowiednich działań, które przyczynią się do poprawy efektywności organizacji i zmniejszenia kosztów. Niniejsze badanie eksploacyjne przeprowadzono na zestawie danych pochodzącym z działu HR.
1. Opis i struktura danych
| Age | Attrition | BusinessTravel | DailyRate | Department | DistanceFromHome | Education | EducationField | EmployeeCount | EmployeeNumber | EnvironmentSatisfaction | Gender | HourlyRate | JobInvolvement | JobLevel | JobRole | JobSatisfaction | MaritalStatus | MonthlyIncome | MonthlyRate | NumCompaniesWorked | Over18 | OverTime | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| NA | Yes | Travel_Rarely | 1102 | Sales | 1 | 2 | Life Sciences | 1 | 1 | 2 | Female | 94 | 3 | 2 | Sales Executive | 4 | Single | 5993 | 19479 | 8 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 8 | 0 | 1 | 6 | 4 | 0 | 5 |
| 49 | No | Travel_Frequently | 279 | Research & Development | 8 | 1 | Life Sciences | 1 | 2 | 3 | Male | 61 | 2 | 2 | Research Scientist | 2 | Married | NA | 24907 | 1 | Y | No | 23 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 1 | 7 |
| 37 | NA | Travel_Rarely | 1373 | Research & Development | 2 | 2 | Other | 1 | 4 | 4 | Male | 92 | 2 | 1 | Laboratory Technician | 3 | Single | 2090 | 2396 | 6 | Y | Yes | 15 | 3 | 2 | 80 | 0 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 33 | No | Travel_Frequently | 1392 | Research & Development | 3 | 4 | Life Sciences | 1 | 5 | 4 | Female | 56 | 3 | 1 | Research Scientist | 3 | Married | NA | 23159 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 8 | 7 | 3 | 0 |
| 27 | No | Travel_Rarely | 591 | Research & Development | 2 | 1 | Medical | 1 | 7 | 1 | Male | 40 | 3 | 1 | Laboratory Technician | 2 | Married | 3468 | 16632 | 9 | Y | No | 12 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| 32 | No | Travel_Frequently | 1005 | Research & Development | 2 | 2 | Life Sciences | 1 | 8 | 4 | Male | 79 | 3 | 1 | Laboratory Technician | 4 | Single | 3068 | 11864 | 0 | Y | No | 13 | 3 | 3 | 80 | 0 | 8 | 2 | 2 | 7 | 7 | 3 | 6 |
| 59 | No | Travel_Rarely | 1324 | Research & Development | 3 | 3 | Medical | 1 | 10 | 3 | Female | 81 | 4 | 1 | Laboratory Technician | 1 | Married | 2670 | 9964 | 4 | Y | Yes | 20 | 4 | 1 | 80 | 3 | 12 | 3 | 2 | 1 | 0 | 0 | 0 |
| 30 | No | Travel_Rarely | 1358 | Research & Development | 24 | 1 | Life Sciences | 1 | 11 | 4 | Male | 67 | 3 | 1 | Laboratory Technician | 3 | Divorced | 2693 | 13335 | 1 | Y | No | 22 | 4 | 2 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 38 | No | Travel_Frequently | 216 | Research & Development | 23 | 3 | Life Sciences | 1 | 12 | 4 | Male | 44 | 2 | 3 | Manufacturing Director | 3 | Single | 9526 | 8787 | 0 | Y | No | 21 | 4 | 2 | 80 | 0 | 10 | 2 | 3 | 9 | 7 | 1 | 8 |
| 36 | No | Travel_Rarely | 1299 | Research & Development | 27 | 3 | Medical | 1 | 13 | 3 | Male | 94 | 3 | 2 | Healthcare Representative | 3 | Married | 5237 | 16577 | 6 | Y | No | 13 | 3 | 2 | 80 | 2 | 17 | 3 | 2 | 7 | 7 | 7 | 7 |
| 35 | No | Travel_Rarely | 809 | Research & Development | 16 | 3 | Medical | 1 | 14 | 1 | Male | 84 | 4 | 1 | Laboratory Technician | 2 | Married | 2426 | 16479 | 0 | Y | No | 13 | 3 | 3 | 80 | 1 | 6 | 5 | 3 | 5 | 4 | 0 | 3 |
| 29 | No | Travel_Rarely | 153 | Research & Development | 15 | 2 | Life Sciences | 1 | 15 | 4 | Female | 49 | 2 | 2 | Laboratory Technician | 3 | Single | 4193 | 12682 | 0 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 10 | 3 | 3 | 9 | 5 | 0 | 8 |
| 31 | No | Travel_Rarely | 670 | Research & Development | 26 | 1 | Life Sciences | 1 | 16 | 1 | Male | 31 | 3 | 1 | Research Scientist | 3 | Divorced | 2911 | 15170 | 1 | Y | No | 17 | 3 | 4 | 80 | 1 | 5 | 1 | 2 | 5 | 2 | 4 | 3 |
| 34 | No | Travel_Rarely | 1346 | Research & Development | 19 | 2 | Medical | 1 | 18 | 2 | Male | 93 | 3 | 1 | Laboratory Technician | 4 | Divorced | 2661 | 8758 | 0 | Y | No | 11 | 3 | 3 | 80 | 1 | 3 | 2 | 3 | 2 | 2 | 1 | 2 |
| 28 | Yes | Travel_Rarely | 103 | Research & Development | 24 | 3 | Life Sciences | 1 | 19 | 3 | Male | 50 | 2 | 1 | Laboratory Technician | 3 | Single | 2028 | 12947 | 5 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 6 | 4 | 3 | 4 | 2 | 0 | 3 |
| 29 | No | Travel_Rarely | 1389 | Research & Development | 21 | 4 | Life Sciences | 1 | 20 | 2 | Female | 51 | 4 | 3 | Manufacturing Director | 1 | Divorced | 9980 | 10195 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 10 | 1 | 3 | 10 | 9 | 8 | 8 |
| 32 | NA | Travel_Rarely | 334 | Research & Development | 5 | 2 | Life Sciences | 1 | 21 | 1 | Male | 80 | 4 | 1 | Research Scientist | 2 | Divorced | 3298 | 15053 | 0 | Y | Yes | 12 | 3 | 4 | 80 | 2 | 7 | 5 | 2 | 6 | 2 | 0 | 5 |
| 22 | No | Non-Travel | 1123 | Research & Development | 16 | 2 | Medical | 1 | 22 | 4 | Male | 96 | 4 | 1 | Laboratory Technician | 4 | Divorced | 2935 | 7324 | 1 | Y | Yes | 13 | 3 | 2 | 80 | 2 | 1 | 2 | 2 | 1 | 0 | 0 | 0 |
| 53 | No | Travel_Rarely | 1219 | Sales | 2 | 4 | Life Sciences | 1 | 23 | 1 | Female | 78 | 2 | 4 | Manager | 4 | Married | 15427 | 22021 | 2 | Y | No | 16 | 3 | 3 | 80 | 0 | 31 | 3 | 3 | 25 | 8 | 3 | 7 |
| 38 | No | Travel_Rarely | 371 | Research & Development | 2 | 3 | Life Sciences | 1 | 24 | 4 | Male | 45 | 3 | 1 | Research Scientist | 4 | Single | 3944 | 4306 | 5 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 3 | 2 | 1 | 2 |
| 24 | No | Non-Travel | 673 | Research & Development | 11 | 2 | Other | 1 | 26 | 1 | Female | 96 | 4 | 2 | Manufacturing Director | 3 | Divorced | 4011 | 8232 | 0 | Y | No | 18 | 3 | 4 | 80 | 1 | 5 | 5 | 2 | 4 | 2 | 1 | 3 |
| 36 | Yes | Travel_Rarely | 1218 | Sales | 9 | 4 | Life Sciences | 1 | 27 | 3 | Male | 82 | 2 | 1 | Sales Representative | 1 | Single | NA | 6986 | 7 | Y | No | 23 | 4 | 2 | 80 | 0 | 10 | 4 | 3 | 5 | 3 | 0 | 3 |
| 34 | No | Travel_Rarely | 419 | Research & Development | 7 | 4 | Life Sciences | 1 | 28 | 1 | Female | 53 | 3 | 3 | Research Director | 2 | Single | 11994 | 21293 | 0 | Y | No | 11 | 3 | 3 | 80 | 0 | 13 | 4 | 3 | 12 | 6 | 2 | 11 |
| 21 | No | Travel_Rarely | 391 | Research & Development | 15 | 2 | Life Sciences | 1 | 30 | 3 | Male | 96 | 3 | 1 | Research Scientist | 4 | Single | 1232 | 19281 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 0 | 6 | 3 | 0 | 0 | 0 | 0 |
| 34 | Yes | Travel_Rarely | 699 | Research & Development | 6 | 1 | Medical | 1 | 31 | 2 | Male | 83 | 3 | 1 | Research Scientist | 1 | Single | 2960 | 17102 | 2 | Y | No | 11 | 3 | 3 | 80 | 0 | 8 | 2 | 3 | 4 | 2 | 1 | 3 |
| 53 | No | Travel_Rarely | 1282 | Research & Development | 5 | 3 | Other | 1 | 32 | 3 | Female | 58 | 3 | 5 | Manager | 3 | Divorced | 19094 | 10735 | 4 | Y | No | 11 | 3 | 4 | 80 | 1 | 26 | 3 | 2 | 14 | 13 | 4 | 8 |
| 32 | Yes | Travel_Frequently | 1125 | Research & Development | 16 | 1 | Life Sciences | 1 | 33 | 2 | Female | 72 | 1 | 1 | Research Scientist | 1 | Single | 3919 | 4681 | 1 | Y | Yes | 22 | 4 | 2 | 80 | 0 | 10 | 5 | 3 | 10 | 2 | 6 | 7 |
| 42 | No | Travel_Rarely | 691 | Sales | 8 | 4 | Marketing | 1 | 35 | 3 | Male | 48 | 3 | 2 | Sales Executive | 2 | Married | 6825 | 21173 | 0 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 2 | 3 | 9 | 7 | 4 | 2 |
| 44 | No | Travel_Rarely | 477 | Research & Development | 7 | 4 | Medical | 1 | 36 | 1 | Female | 42 | 2 | 3 | Healthcare Representative | 4 | Married | 10248 | 2094 | 3 | Y | No | 14 | 3 | 4 | 80 | 1 | 24 | 4 | 3 | 22 | 6 | 5 | 17 |
| 46 | No | Travel_Rarely | 705 | Sales | 2 | 4 | Marketing | 1 | 38 | 2 | Female | 83 | 3 | 5 | Manager | 1 | Single | 18947 | 22822 | 3 | Y | No | 12 | 3 | 4 | 80 | 0 | 22 | 2 | 2 | 2 | 2 | 2 | 1 |
| 33 | No | Travel_Rarely | 924 | Research & Development | 2 | 3 | Medical | 1 | 39 | 3 | Male | 78 | 3 | 1 | Laboratory Technician | 4 | Single | 2496 | 6670 | 4 | Y | No | 11 | 3 | 4 | 80 | 0 | 7 | 3 | 3 | 1 | 1 | 0 | 0 |
| 44 | No | Travel_Rarely | 1459 | Research & Development | 10 | 4 | Other | 1 | 40 | 4 | Male | 41 | 3 | 2 | Healthcare Representative | 4 | Married | 6465 | 19121 | 2 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 9 | 5 | 4 | 4 | 2 | 1 | 3 |
| 30 | NA | Travel_Rarely | 125 | Research & Development | 9 | 2 | Medical | 1 | 41 | 4 | Male | 83 | 2 | 1 | Laboratory Technician | 3 | Single | 2206 | 16117 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 10 | 5 | 3 | 10 | 0 | 1 | 8 |
| 39 | Yes | Travel_Rarely | 895 | Sales | 5 | 3 | Technical Degree | 1 | 42 | 4 | Male | 56 | 3 | 2 | Sales Representative | 4 | Married | 2086 | 3335 | 3 | Y | No | 14 | 3 | 3 | 80 | 1 | 19 | 6 | 4 | 1 | 0 | 0 | 0 |
| 24 | Yes | Travel_Rarely | 813 | Research & Development | 1 | 3 | Medical | 1 | 45 | 2 | Male | 61 | 3 | 1 | Research Scientist | 4 | Married | 2293 | 3020 | 2 | Y | Yes | 16 | 3 | 1 | 80 | 1 | 6 | 2 | 2 | 2 | 0 | 2 | 0 |
| NA | No | Travel_Rarely | 1273 | Research & Development | 2 | 2 | Medical | 1 | 46 | 4 | Female | 72 | 4 | 1 | Research Scientist | 3 | Divorced | 2645 | 21923 | 1 | Y | No | 12 | 3 | 4 | 80 | 2 | 6 | 3 | 2 | 5 | 3 | 1 | 4 |
| 50 | Yes | Travel_Rarely | 869 | Sales | 3 | 2 | Marketing | 1 | 47 | 1 | Male | 86 | 2 | 1 | Sales Representative | 3 | Married | 2683 | 3810 | 1 | Y | Yes | 14 | 3 | 3 | 80 | 0 | 3 | 2 | 3 | 3 | 2 | 0 | 2 |
| 35 | No | Travel_Rarely | 890 | Sales | 2 | 3 | Marketing | 1 | 49 | 4 | Female | 97 | 3 | 1 | Sales Representative | 4 | Married | NA | 9687 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 2 | 2 |
| 36 | No | Travel_Rarely | 852 | Research & Development | 5 | 4 | Life Sciences | 1 | 51 | 2 | Female | 82 | 2 | 1 | Research Scientist | 1 | Married | 3419 | 13072 | 9 | Y | Yes | 14 | 3 | 4 | 80 | 1 | 6 | 3 | 4 | 1 | 1 | 0 | 0 |
| 33 | No | Travel_Frequently | 1141 | Sales | 1 | 3 | Life Sciences | 1 | 52 | 3 | Female | 42 | 4 | 2 | Sales Executive | 1 | Married | 5376 | 3193 | 2 | Y | No | 19 | 3 | 1 | 80 | 2 | 10 | 3 | 3 | 5 | 3 | 1 | 3 |
| 35 | No | Travel_Rarely | 464 | Research & Development | 4 | 2 | Other | 1 | 53 | 3 | Male | 75 | 3 | 1 | Laboratory Technician | 4 | Divorced | 1951 | 10910 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 27 | No | Travel_Rarely | 1240 | Research & Development | 2 | 4 | Life Sciences | 1 | 54 | 4 | Female | 33 | 3 | 1 | Laboratory Technician | 1 | Divorced | 2341 | 19715 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 1 | 6 | 3 | 1 | 0 | 0 | 0 |
| 26 | Yes | Travel_Rarely | 1357 | Research & Development | 25 | 3 | Life Sciences | 1 | 55 | 1 | Male | 48 | 1 | 1 | Laboratory Technician | 3 | Single | 2293 | 10558 | 1 | Y | No | 12 | 3 | 3 | 80 | 0 | 1 | 2 | 2 | 1 | 0 | 0 | 1 |
| 27 | No | Travel_Frequently | 994 | Sales | 8 | 3 | Life Sciences | 1 | 56 | 4 | Male | 37 | 3 | 3 | Sales Executive | 3 | Single | 8726 | 2975 | 1 | Y | No | 15 | 3 | 4 | 80 | 0 | 9 | 0 | 3 | 9 | 8 | 1 | 7 |
| 30 | No | Travel_Frequently | 721 | Research & Development | 1 | 2 | Medical | 1 | 57 | 3 | Female | 58 | 3 | 2 | Laboratory Technician | 4 | Single | 4011 | 10781 | 1 | Y | No | 23 | 4 | 4 | 80 | 0 | 12 | 2 | 3 | 12 | 8 | 3 | 7 |
| 41 | Yes | Travel_Rarely | 1360 | Research & Development | 12 | 3 | Technical Degree | 1 | 58 | 2 | Female | 49 | 3 | 5 | Research Director | 3 | Married | 19545 | 16280 | 1 | Y | No | 12 | 3 | 4 | 80 | 0 | 23 | 0 | 3 | 22 | 15 | 15 | 8 |
| 34 | No | Non-Travel | 1065 | Sales | 23 | 4 | Marketing | 1 | 60 | 2 | Male | 72 | 3 | 2 | Sales Executive | 3 | Single | 4568 | 10034 | 0 | Y | No | 20 | 4 | 3 | 80 | 0 | 10 | 2 | 3 | 9 | 5 | 8 | 7 |
| 37 | No | Travel_Rarely | 408 | Research & Development | 19 | 2 | Life Sciences | 1 | 61 | 2 | Male | 73 | 3 | 1 | Research Scientist | 2 | Married | 3022 | 10227 | 4 | Y | No | 21 | 4 | 1 | 80 | 0 | 8 | 1 | 3 | 1 | 0 | 0 | 0 |
| 46 | No | Travel_Frequently | 1211 | Sales | 5 | 4 | Marketing | 1 | 62 | 1 | Male | 98 | 3 | 2 | Sales Executive | 4 | Single | 5772 | 20445 | 4 | Y | Yes | 21 | 4 | 3 | 80 | 0 | 14 | 4 | 3 | 9 | 6 | 0 | 8 |
| 35 | No | Travel_Rarely | 1229 | Research & Development | 8 | 1 | Life Sciences | 1 | 63 | 4 | Male | 36 | 4 | 1 | Laboratory Technician | 4 | Married | 2269 | 4892 | 1 | Y | No | 19 | 3 | 4 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 1 |
| 48 | Yes | Travel_Rarely | 626 | Research & Development | 1 | 2 | Life Sciences | 1 | 64 | 1 | Male | 98 | 2 | 3 | Laboratory Technician | 3 | Single | NA | 19294 | 9 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 23 | 2 | 3 | 1 | 0 | 0 | 0 |
| 28 | Yes | Travel_Rarely | 1434 | Research & Development | 5 | 4 | Technical Degree | 1 | 65 | 3 | Male | 50 | 3 | 1 | Laboratory Technician | 3 | Single | 3441 | 11179 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 2 | 3 | 2 | 2 | 2 | 2 | 2 |
| 44 | No | Travel_Rarely | 1488 | Sales | 1 | 5 | Marketing | 1 | 68 | 2 | Female | 75 | 3 | 2 | Sales Executive | 1 | Divorced | 5454 | 4009 | 5 | Y | Yes | 21 | 4 | 3 | 80 | 1 | 9 | 2 | 2 | 4 | 3 | 1 | 3 |
| 35 | No | Non-Travel | 1097 | Research & Development | 11 | 2 | Medical | 1 | 70 | 3 | Male | 79 | 2 | 3 | Healthcare Representative | 1 | Married | 9884 | 8302 | 2 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 10 | 3 | 3 | 4 | 0 | 2 | 3 |
| 26 | No | Travel_Rarely | 1443 | Sales | 23 | 3 | Marketing | 1 | 72 | 3 | Female | 47 | 2 | 2 | Sales Executive | 4 | Married | 4157 | 21436 | 7 | Y | Yes | 19 | 3 | 3 | 80 | 1 | 5 | 2 | 2 | 2 | 2 | 0 | 0 |
| 33 | No | Travel_Frequently | 515 | Research & Development | 1 | 2 | Life Sciences | 1 | 73 | 1 | Female | 98 | 3 | 3 | Research Director | 4 | Single | 13458 | 15146 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 15 | 1 | 3 | 15 | 14 | 8 | 12 |
| 35 | NA | Travel_Frequently | 853 | Sales | 18 | 5 | Life Sciences | 1 | 74 | 2 | Male | 71 | 3 | 3 | Sales Executive | 1 | Married | 9069 | 11031 | 1 | Y | No | 22 | 4 | 4 | 80 | 1 | 9 | 3 | 2 | 9 | 8 | 1 | 8 |
| 35 | No | Travel_Rarely | 1142 | Research & Development | 23 | 4 | Medical | 1 | 75 | 3 | Female | 30 | 3 | 1 | Laboratory Technician | 1 | Married | 4014 | 16002 | 3 | Y | Yes | 15 | 3 | 3 | 80 | 1 | 4 | 3 | 3 | 2 | 2 | 2 | 2 |
| 31 | No | Travel_Rarely | 655 | Research & Development | 7 | 4 | Life Sciences | 1 | 76 | 4 | Male | 48 | 3 | 2 | Laboratory Technician | 4 | Divorced | 5915 | 9528 | 3 | Y | No | 22 | 4 | 4 | 80 | 1 | 10 | 3 | 2 | 7 | 7 | 1 | 7 |
| 37 | No | Travel_Rarely | 1115 | Research & Development | 1 | 4 | Life Sciences | 1 | 77 | 1 | Male | 51 | 2 | 2 | Manufacturing Director | 3 | Divorced | 5993 | 2689 | 1 | Y | No | 18 | 3 | 3 | 80 | 1 | 7 | 2 | 4 | 7 | 5 | 0 | 7 |
| 32 | No | Travel_Rarely | 427 | Research & Development | 1 | 3 | Medical | 1 | 78 | 1 | Male | 33 | 3 | 2 | Manufacturing Director | 4 | Married | 6162 | 10877 | 1 | Y | Yes | 22 | 4 | 2 | 80 | 1 | 9 | 3 | 3 | 9 | 8 | 7 | 8 |
| NA | NA | Travel_Frequently | 653 | Research & Development | 29 | 5 | Life Sciences | 1 | 79 | 4 | Female | 50 | 3 | 2 | Laboratory Technician | 4 | Single | 2406 | 5456 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 10 | 3 | 9 | 9 |
| 50 | No | Travel_Rarely | 989 | Research & Development | 7 | 2 | Medical | 1 | 80 | 2 | Female | 43 | 2 | 5 | Research Director | 3 | Divorced | 18740 | 16701 | 5 | Y | Yes | 12 | 3 | 4 | 80 | 1 | 29 | 2 | 2 | 27 | 3 | 13 | 8 |
| 59 | No | Travel_Rarely | 1435 | Sales | 25 | 3 | Life Sciences | 1 | 81 | 1 | Female | 99 | 3 | 3 | Sales Executive | 1 | Single | 7637 | 2354 | 7 | Y | No | 11 | 3 | 4 | 80 | 0 | 28 | 3 | 2 | 21 | 16 | 7 | 9 |
| 36 | No | Travel_Rarely | 1223 | Research & Development | 8 | 3 | Technical Degree | 1 | 83 | 3 | Female | 59 | 3 | 3 | Healthcare Representative | 3 | Divorced | 10096 | 8202 | 1 | Y | No | 13 | 3 | 2 | 80 | 3 | 17 | 2 | 3 | 17 | 14 | 12 | 8 |
| NA | No | Travel_Rarely | 836 | Research & Development | 8 | 3 | Medical | 1 | 84 | 4 | Female | 33 | 3 | 4 | Manager | 3 | Divorced | 14756 | 19730 | 2 | Y | Yes | 14 | 3 | 3 | 80 | 3 | 21 | 2 | 3 | 5 | 0 | 0 | 2 |
| NA | No | Travel_Frequently | 1195 | Research & Development | 11 | 3 | Life Sciences | 1 | 85 | 2 | Male | 95 | 2 | 2 | Manufacturing Director | 2 | Single | 6499 | 22656 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 6 | 5 | 0 | 3 |
| 45 | No | Travel_Rarely | 1339 | Research & Development | 7 | 3 | Life Sciences | 1 | 86 | 2 | Male | 59 | 3 | 3 | Research Scientist | 1 | Divorced | 9724 | 18787 | 2 | Y | No | 17 | 3 | 3 | 80 | 1 | 25 | 2 | 3 | 1 | 0 | 0 | 0 |
| 35 | No | Travel_Frequently | 664 | Research & Development | 1 | 3 | Medical | 1 | 88 | 2 | Male | 79 | 3 | 1 | Research Scientist | 1 | Married | 2194 | 5868 | 4 | Y | No | 13 | 3 | 4 | 80 | 1 | 5 | 2 | 2 | 3 | 2 | 1 | 2 |
| 36 | Yes | Travel_Rarely | 318 | Research & Development | 9 | 3 | Medical | 1 | 90 | 4 | Male | 79 | 2 | 1 | Research Scientist | 3 | Married | 3388 | 21777 | 0 | Y | Yes | 17 | 3 | 1 | 80 | 1 | 2 | 0 | 2 | 1 | 0 | 0 | 0 |
| 59 | No | Travel_Frequently | 1225 | Sales | 1 | 1 | Life Sciences | 1 | 91 | 1 | Female | 57 | 2 | 2 | Sales Executive | 3 | Single | 5473 | 24668 | 7 | Y | No | 11 | 3 | 4 | 80 | 0 | 20 | 2 | 2 | 4 | 3 | 1 | 3 |
| 29 | NA | Travel_Rarely | 1328 | Research & Development | 2 | 3 | Life Sciences | 1 | 94 | 3 | Male | 76 | 3 | 1 | Research Scientist | 2 | Married | 2703 | 4956 | 0 | Y | No | 23 | 4 | 4 | 80 | 1 | 6 | 3 | 3 | 5 | 4 | 0 | 4 |
| 31 | No | Travel_Rarely | 1082 | Research & Development | 1 | 4 | Medical | 1 | 95 | 3 | Male | 87 | 3 | 1 | Research Scientist | 2 | Single | 2501 | 18775 | 1 | Y | No | 17 | 3 | 2 | 80 | 0 | 1 | 4 | 3 | 1 | 1 | 1 | 0 |
| 32 | No | Travel_Rarely | 548 | Research & Development | 1 | 3 | Life Sciences | 1 | 96 | 2 | Male | 66 | 3 | 2 | Research Scientist | 2 | Married | NA | 7346 | 1 | Y | No | 17 | 3 | 2 | 80 | 2 | 10 | 3 | 3 | 10 | 4 | 0 | 9 |
| 36 | No | Travel_Rarely | 132 | Research & Development | 6 | 3 | Life Sciences | 1 | 97 | 2 | Female | 55 | 4 | 1 | Laboratory Technician | 4 | Married | 3038 | 22002 | 3 | Y | No | 12 | 3 | 2 | 80 | 0 | 5 | 3 | 3 | 1 | 0 | 0 | 0 |
| 31 | No | Travel_Rarely | 746 | Research & Development | 8 | 4 | Life Sciences | 1 | 98 | 3 | Female | 61 | 3 | 2 | Manufacturing Director | 4 | Single | 4424 | 20682 | 1 | Y | No | 23 | 4 | 4 | 80 | 0 | 11 | 2 | 3 | 11 | 7 | 1 | 8 |
| 35 | No | Travel_Rarely | 776 | Sales | 1 | 4 | Marketing | 1 | 100 | 3 | Male | 32 | 2 | 2 | Sales Executive | 1 | Single | 4312 | 23016 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 16 | 2 | 3 | 15 | 13 | 2 | 8 |
| 45 | No | Travel_Rarely | 193 | Research & Development | 6 | 4 | Other | 1 | 101 | 4 | Male | 52 | 3 | 3 | Research Director | 1 | Married | NA | 15067 | 4 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 17 | 3 | 4 | 0 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 397 | Research & Development | 7 | 4 | Medical | 1 | 102 | 1 | Male | 30 | 3 | 3 | Research Director | 3 | Single | 13664 | 25258 | 4 | Y | No | 13 | 3 | 1 | 80 | 0 | 16 | 3 | 4 | 5 | 2 | 0 | 2 |
| 46 | No | Travel_Rarely | 945 | Human Resources | 5 | 2 | Medical | 1 | 103 | 2 | Male | 80 | 3 | 2 | Human Resources | 2 | Divorced | 5021 | 10425 | 8 | Y | Yes | 22 | 4 | 4 | 80 | 1 | 16 | 2 | 3 | 4 | 2 | 0 | 2 |
| 30 | No | Travel_Rarely | 852 | Research & Development | 1 | 1 | Life Sciences | 1 | 104 | 4 | Male | 55 | 2 | 2 | Laboratory Technician | 4 | Married | NA | 15998 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 2 | 10 | 1 | 2 | 10 | 8 | 3 | 0 |
| 35 | No | Travel_Rarely | 1214 | Research & Development | 1 | 3 | Medical | 1 | 105 | 2 | Male | 30 | 2 | 1 | Research Scientist | 3 | Single | NA | 26278 | 1 | Y | No | 18 | 3 | 1 | 80 | 0 | 6 | 3 | 3 | 6 | 4 | 0 | 4 |
| 55 | No | Travel_Rarely | 111 | Sales | 1 | 2 | Life Sciences | 1 | 106 | 1 | Male | 70 | 3 | 3 | Sales Executive | 4 | Married | 10239 | 18092 | 3 | Y | No | 14 | 3 | 4 | 80 | 1 | 24 | 4 | 3 | 1 | 0 | 1 | 0 |
| 38 | No | Non-Travel | 573 | Research & Development | 6 | 3 | Medical | 1 | 107 | 2 | Female | 79 | 1 | 2 | Research Scientist | 4 | Divorced | 5329 | 15717 | 7 | Y | Yes | 12 | 3 | 4 | 80 | 3 | 17 | 3 | 3 | 13 | 11 | 1 | 9 |
| 34 | No | Travel_Rarely | 1153 | Research & Development | 1 | 2 | Medical | 1 | 110 | 1 | Male | 94 | 3 | 2 | Manufacturing Director | 2 | Married | 4325 | 17736 | 1 | Y | No | 15 | 3 | 3 | 80 | 0 | 5 | 2 | 3 | 5 | 2 | 1 | 3 |
| 56 | No | Travel_Rarely | 1400 | Research & Development | 7 | 3 | Life Sciences | 1 | 112 | 4 | Male | 49 | 1 | 3 | Manufacturing Director | 4 | Single | NA | 21698 | 4 | Y | No | 11 | 3 | 1 | 80 | 0 | 37 | 3 | 2 | 6 | 4 | 0 | 2 |
| 23 | No | Travel_Rarely | 541 | Sales | 2 | 1 | Technical Degree | 1 | 113 | 3 | Male | 62 | 3 | 1 | Sales Representative | 1 | Divorced | 2322 | 9518 | 3 | Y | No | 13 | 3 | 3 | 80 | 1 | 3 | 3 | 3 | 0 | 0 | 0 | 0 |
| 51 | No | Travel_Rarely | 432 | Research & Development | 9 | 4 | Life Sciences | 1 | 116 | 4 | Male | 96 | 3 | 1 | Laboratory Technician | 4 | Married | 2075 | 18725 | 3 | Y | No | 23 | 4 | 2 | 80 | 2 | 10 | 4 | 3 | 4 | 2 | 0 | 3 |
| 30 | No | Travel_Rarely | 288 | Research & Development | 2 | 3 | Life Sciences | 1 | 117 | 3 | Male | 99 | 2 | 2 | Healthcare Representative | 4 | Married | 4152 | 15830 | 1 | Y | No | 19 | 3 | 1 | 80 | 3 | 11 | 3 | 3 | 11 | 10 | 10 | 8 |
| 46 | Yes | Travel_Rarely | 669 | Sales | 9 | 2 | Medical | 1 | 118 | 3 | Male | 64 | 2 | 3 | Sales Executive | 4 | Single | 9619 | 13596 | 1 | Y | No | 16 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 9 | 8 | 4 | 7 |
| 40 | No | Travel_Frequently | 530 | Research & Development | 1 | 4 | Life Sciences | 1 | 119 | 3 | Male | 78 | 2 | 4 | Healthcare Representative | 2 | Married | 13503 | 14115 | 1 | Y | No | 22 | 4 | 4 | 80 | 1 | 22 | 3 | 2 | 22 | 3 | 11 | 11 |
| 51 | No | Travel_Rarely | 632 | Sales | 21 | 4 | Marketing | 1 | 120 | 3 | Male | 71 | 3 | 2 | Sales Executive | 4 | Single | 5441 | 8423 | 0 | Y | Yes | 22 | 4 | 4 | 80 | 0 | 11 | 2 | 1 | 10 | 7 | 1 | 0 |
| 30 | No | Travel_Rarely | 1334 | Sales | 4 | 2 | Medical | 1 | 121 | 3 | Female | 63 | 2 | 2 | Sales Executive | 2 | Divorced | 5209 | 19760 | 1 | Y | Yes | 12 | 3 | 2 | 80 | 3 | 11 | 4 | 2 | 11 | 8 | 2 | 7 |
| 46 | No | Travel_Frequently | 638 | Research & Development | 1 | 3 | Medical | 1 | 124 | 3 | Male | 40 | 2 | 3 | Healthcare Representative | 1 | Married | 10673 | 3142 | 2 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 21 | 5 | 2 | 10 | 9 | 9 | 5 |
| 32 | No | Travel_Rarely | 1093 | Sales | 6 | 4 | Medical | 1 | 125 | 2 | Male | 87 | 3 | 2 | Sales Executive | 3 | Single | 5010 | 24301 | 1 | Y | No | 16 | 3 | 1 | 80 | 0 | 12 | 0 | 3 | 11 | 8 | 5 | 7 |
| 54 | No | Travel_Rarely | 1217 | Research & Development | 2 | 4 | Technical Degree | 1 | 126 | 1 | Female | 60 | 3 | 3 | Research Director | 3 | Married | 13549 | 24001 | 9 | Y | No | 12 | 3 | 1 | 80 | 1 | 16 | 5 | 1 | 4 | 3 | 0 | 3 |
| 24 | No | Travel_Rarely | 1353 | Sales | 3 | 2 | Other | 1 | 128 | 1 | Female | 33 | 3 | 2 | Sales Executive | 3 | Married | 4999 | 17519 | 0 | Y | No | 21 | 4 | 1 | 80 | 1 | 4 | 2 | 2 | 3 | 2 | 0 | 2 |
| 28 | No | Non-Travel | 120 | Sales | 4 | 3 | Medical | 1 | 129 | 2 | Male | 43 | 3 | 2 | Sales Executive | 3 | Married | 4221 | 8863 | 1 | Y | No | 15 | 3 | 2 | 80 | 0 | 5 | 3 | 4 | 5 | 4 | 0 | 4 |
| 58 | No | Travel_Rarely | 682 | Sales | 10 | 4 | Medical | 1 | 131 | 4 | Male | 37 | 3 | 4 | Sales Executive | 3 | Single | 13872 | 24409 | 0 | Y | No | 13 | 3 | 3 | 80 | 0 | 38 | 1 | 2 | 37 | 10 | 1 | 8 |
| 44 | No | Non-Travel | 489 | Research & Development | 23 | 3 | Medical | 1 | 132 | 2 | Male | 67 | 3 | 2 | Laboratory Technician | 2 | Married | 2042 | 25043 | 4 | Y | No | 12 | 3 | 3 | 80 | 1 | 17 | 3 | 4 | 3 | 2 | 1 | 2 |
| 37 | Yes | Travel_Rarely | 807 | Human Resources | 6 | 4 | Human Resources | 1 | 133 | 3 | Male | 63 | 3 | 1 | Human Resources | 1 | Divorced | 2073 | 23648 | 4 | Y | Yes | 22 | 4 | 4 | 80 | 0 | 7 | 3 | 3 | 3 | 2 | 0 | 2 |
| 32 | NA | Travel_Rarely | 827 | Research & Development | 1 | 1 | Life Sciences | 1 | 134 | 4 | Male | 71 | 3 | 1 | Research Scientist | 1 | Single | 2956 | 15178 | 1 | Y | No | 13 | 3 | 4 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 20 | Yes | Travel_Frequently | 871 | Research & Development | 6 | 3 | Life Sciences | 1 | 137 | 4 | Female | 66 | 2 | 1 | Laboratory Technician | 4 | Single | 2926 | 19783 | 1 | Y | Yes | 18 | 3 | 2 | 80 | 0 | 1 | 5 | 3 | 1 | 0 | 1 | 0 |
| 34 | NA | Travel_Rarely | 665 | Research & Development | 6 | 4 | Other | 1 | 138 | 1 | Female | 41 | 3 | 2 | Research Scientist | 3 | Single | 4809 | 12482 | 1 | Y | No | 14 | 3 | 3 | 80 | 0 | 16 | 3 | 3 | 16 | 13 | 2 | 10 |
| 37 | No | Non-Travel | 1040 | Research & Development | 2 | 2 | Life Sciences | 1 | 139 | 3 | Male | 100 | 2 | 2 | Healthcare Representative | 4 | Divorced | NA | 15850 | 5 | Y | No | 14 | 3 | 4 | 80 | 1 | 17 | 2 | 4 | 1 | 0 | 0 | 0 |
| 59 | No | Non-Travel | 1420 | Human Resources | 2 | 4 | Human Resources | 1 | 140 | 3 | Female | 32 | 2 | 5 | Manager | 4 | Married | 18844 | 21922 | 9 | Y | No | 21 | 4 | 4 | 80 | 1 | 30 | 3 | 3 | 3 | 2 | 2 | 2 |
| 50 | NA | Travel_Frequently | 1115 | Research & Development | 1 | 3 | Life Sciences | 1 | 141 | 1 | Female | 73 | 3 | 5 | Research Director | 2 | Married | 18172 | 9755 | 3 | Y | Yes | 19 | 3 | 1 | 80 | 0 | 28 | 1 | 2 | 8 | 3 | 0 | 7 |
| 25 | NA | Travel_Rarely | 240 | Sales | 5 | 3 | Marketing | 1 | 142 | 3 | Male | 46 | 2 | 2 | Sales Executive | 3 | Single | 5744 | 26959 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 6 | 1 | 3 | 6 | 4 | 0 | 3 |
| 25 | No | Travel_Rarely | 1280 | Research & Development | 7 | 1 | Medical | 1 | 143 | 4 | Male | 64 | 2 | 1 | Research Scientist | 4 | Married | 2889 | 26897 | 1 | Y | No | 11 | 3 | 3 | 80 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 1 |
| 22 | NA | Travel_Rarely | 534 | Research & Development | 15 | 3 | Medical | 1 | 144 | 2 | Female | 59 | 3 | 1 | Laboratory Technician | 4 | Single | 2871 | 23785 | 1 | Y | No | 15 | 3 | 3 | 80 | 0 | 1 | 5 | 3 | 0 | 0 | 0 | 0 |
| 51 | No | Travel_Frequently | 1456 | Research & Development | 1 | 4 | Medical | 1 | 145 | 1 | Female | 30 | 2 | 3 | Healthcare Representative | 1 | Single | 7484 | 25796 | 3 | Y | No | 20 | 4 | 3 | 80 | 0 | 23 | 1 | 2 | 13 | 12 | 12 | 8 |
| 34 | NA | Travel_Frequently | 658 | Research & Development | 7 | 3 | Life Sciences | 1 | 147 | 1 | Male | 66 | 1 | 2 | Laboratory Technician | 3 | Single | NA | 22887 | 1 | Y | Yes | 24 | 4 | 4 | 80 | 0 | 9 | 3 | 3 | 9 | 7 | 0 | 6 |
| 54 | No | Non-Travel | 142 | Human Resources | 26 | 3 | Human Resources | 1 | 148 | 4 | Female | 30 | 4 | 4 | Manager | 4 | Single | 17328 | 13871 | 2 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 23 | 3 | 3 | 5 | 3 | 4 | 4 |
| 24 | No | Travel_Rarely | 1127 | Research & Development | 18 | 1 | Life Sciences | 1 | 150 | 2 | Male | 52 | 3 | 1 | Laboratory Technician | 3 | Married | NA | 13257 | 0 | Y | No | 12 | 3 | 3 | 80 | 1 | 6 | 2 | 3 | 5 | 3 | 1 | 2 |
| 34 | No | Travel_Rarely | 1031 | Research & Development | 6 | 4 | Life Sciences | 1 | 151 | 3 | Female | 45 | 2 | 2 | Research Scientist | 2 | Divorced | 4505 | 15000 | 6 | Y | No | 15 | 3 | 3 | 80 | 1 | 12 | 3 | 3 | 1 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 1189 | Sales | 3 | 3 | Life Sciences | 1 | 152 | 3 | Male | 87 | 3 | 3 | Sales Executive | 4 | Single | NA | 14506 | 2 | Y | No | 12 | 3 | 1 | 80 | 0 | 12 | 3 | 3 | 5 | 3 | 1 | 3 |
| 34 | No | Travel_Rarely | 1354 | Research & Development | 5 | 3 | Medical | 1 | 153 | 3 | Female | 45 | 2 | 3 | Manager | 1 | Single | 11631 | 5615 | 2 | Y | No | 12 | 3 | 4 | 80 | 0 | 14 | 6 | 3 | 11 | 10 | 5 | 8 |
| 36 | No | Travel_Frequently | 1467 | Sales | 11 | 2 | Technical Degree | 1 | 154 | 2 | Female | 92 | 3 | 3 | Sales Executive | 4 | Married | 9738 | 22952 | 0 | Y | No | 14 | 3 | 3 | 80 | 1 | 10 | 6 | 3 | 9 | 7 | 2 | 8 |
| 36 | No | Travel_Rarely | 922 | Research & Development | 3 | 2 | Life Sciences | 1 | 155 | 1 | Female | 39 | 3 | 1 | Laboratory Technician | 4 | Divorced | 2835 | 2561 | 5 | Y | No | 22 | 4 | 1 | 80 | 1 | 7 | 2 | 3 | 1 | 0 | 0 | 0 |
| 43 | No | Travel_Frequently | 394 | Sales | 26 | 2 | Life Sciences | 1 | 158 | 3 | Male | 92 | 3 | 4 | Manager | 4 | Married | NA | 19494 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 2 | 25 | 3 | 4 | 25 | 12 | 4 | 12 |
| 30 | NA | Travel_Frequently | 1312 | Research & Development | 23 | 3 | Life Sciences | 1 | 159 | 1 | Male | 96 | 1 | 1 | Research Scientist | 3 | Divorced | 2613 | 22310 | 1 | Y | No | 25 | 4 | 3 | 80 | 3 | 10 | 2 | 2 | 10 | 7 | 0 | 9 |
| 33 | No | Non-Travel | 750 | Sales | 22 | 2 | Marketing | 1 | 160 | 3 | Male | 95 | 3 | 2 | Sales Executive | 2 | Married | 6146 | 15480 | 0 | Y | No | 13 | 3 | 1 | 80 | 1 | 8 | 2 | 4 | 7 | 7 | 0 | 7 |
| 56 | Yes | Travel_Rarely | 441 | Research & Development | 14 | 4 | Life Sciences | 1 | 161 | 2 | Female | 72 | 3 | 1 | Research Scientist | 2 | Married | 4963 | 4510 | 9 | Y | Yes | 18 | 3 | 1 | 80 | 3 | 7 | 2 | 3 | 5 | 4 | 4 | 3 |
| 51 | No | Travel_Rarely | 684 | Research & Development | 6 | 3 | Life Sciences | 1 | 162 | 1 | Male | 51 | 3 | 5 | Research Director | 3 | Single | 19537 | 6462 | 7 | Y | No | 13 | 3 | 3 | 80 | 0 | 23 | 5 | 3 | 20 | 18 | 15 | 15 |
| 31 | Yes | Travel_Rarely | 249 | Sales | 6 | 4 | Life Sciences | 1 | 163 | 2 | Male | 76 | 1 | 2 | Sales Executive | 3 | Married | 6172 | 20739 | 4 | Y | Yes | 18 | 3 | 2 | 80 | 0 | 12 | 3 | 2 | 7 | 7 | 7 | 7 |
| 26 | No | Travel_Rarely | 841 | Research & Development | 6 | 3 | Other | 1 | 164 | 3 | Female | 46 | 2 | 1 | Research Scientist | 2 | Married | 2368 | 23300 | 1 | Y | No | 19 | 3 | 3 | 80 | 0 | 5 | 3 | 2 | 5 | 4 | 4 | 3 |
| 58 | Yes | Travel_Rarely | 147 | Research & Development | 23 | 4 | Medical | 1 | 165 | 4 | Female | 94 | 3 | 3 | Healthcare Representative | 4 | Married | 10312 | 3465 | 1 | Y | No | 12 | 3 | 4 | 80 | 1 | 40 | 3 | 2 | 40 | 10 | 15 | 6 |
| 19 | Yes | Travel_Rarely | 528 | Sales | 22 | 1 | Marketing | 1 | 167 | 4 | Male | 50 | 3 | 1 | Sales Representative | 3 | Single | 1675 | 26820 | 1 | Y | Yes | 19 | 3 | 4 | 80 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 594 | Research & Development | 2 | 1 | Technical Degree | 1 | 169 | 3 | Male | 100 | 3 | 1 | Laboratory Technician | 4 | Married | 2523 | 19299 | 0 | Y | No | 14 | 3 | 3 | 80 | 1 | 3 | 2 | 3 | 2 | 1 | 2 | 1 |
| 49 | No | Travel_Rarely | 470 | Research & Development | 20 | 4 | Medical | 1 | 170 | 3 | Female | 96 | 3 | 2 | Manufacturing Director | 1 | Married | 6567 | 5549 | 1 | Y | No | 14 | 3 | 3 | 80 | 0 | 16 | 2 | 2 | 15 | 11 | 5 | 11 |
| 43 | No | Travel_Frequently | 957 | Research & Development | 28 | 3 | Medical | 1 | 171 | 2 | Female | 72 | 4 | 1 | Research Scientist | 3 | Single | 4739 | 16090 | 4 | Y | No | 12 | 3 | 4 | 80 | 0 | 18 | 2 | 3 | 3 | 2 | 1 | 2 |
| 50 | NA | Travel_Frequently | 809 | Sales | 12 | 3 | Marketing | 1 | 174 | 3 | Female | 77 | 3 | 3 | Sales Executive | 4 | Single | 9208 | 6645 | 4 | Y | No | 11 | 3 | 4 | 80 | 0 | 16 | 3 | 3 | 2 | 2 | 2 | 1 |
| 31 | Yes | Travel_Rarely | 542 | Sales | 20 | 3 | Life Sciences | 1 | 175 | 2 | Female | 71 | 1 | 2 | Sales Executive | 3 | Married | 4559 | 24788 | 3 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 4 | 2 | 3 | 2 | 2 | 2 | 2 |
| NA | No | Travel_Rarely | 802 | Sales | 9 | 1 | Life Sciences | 1 | 176 | 3 | Male | 96 | 3 | 3 | Sales Executive | 3 | Divorced | 8189 | 21196 | 3 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 12 | 2 | 3 | 9 | 7 | 0 | 7 |
| 26 | No | Travel_Rarely | 1355 | Human Resources | 25 | 1 | Life Sciences | 1 | 177 | 3 | Female | 61 | 3 | 1 | Human Resources | 3 | Married | 2942 | 8916 | 1 | Y | No | 23 | 4 | 4 | 80 | 1 | 8 | 3 | 3 | 8 | 7 | 5 | 7 |
| 36 | No | Travel_Rarely | 216 | Research & Development | 6 | 2 | Medical | 1 | 178 | 2 | Male | 84 | 3 | 2 | Manufacturing Director | 2 | Divorced | 4941 | 2819 | 6 | Y | No | 20 | 4 | 4 | 80 | 2 | 7 | 0 | 3 | 3 | 2 | 0 | 1 |
| 51 | Yes | Travel_Frequently | 1150 | Research & Development | 8 | 4 | Life Sciences | 1 | 179 | 1 | Male | 53 | 1 | 3 | Manufacturing Director | 4 | Single | 10650 | 25150 | 2 | Y | No | 15 | 3 | 4 | 80 | 0 | 18 | 2 | 3 | 4 | 2 | 0 | 3 |
| 39 | No | Travel_Rarely | 1329 | Sales | 4 | 4 | Life Sciences | 1 | 182 | 4 | Female | 47 | 2 | 2 | Sales Executive | 3 | Married | NA | 14590 | 4 | Y | No | 14 | 3 | 3 | 80 | 1 | 17 | 1 | 4 | 15 | 11 | 5 | 9 |
| 25 | No | Travel_Rarely | 959 | Sales | 28 | 3 | Life Sciences | 1 | 183 | 1 | Male | 41 | 2 | 2 | Sales Executive | 3 | Married | 8639 | 24835 | 2 | Y | No | 18 | 3 | 4 | 80 | 0 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| 30 | No | Travel_Rarely | 1240 | Human Resources | 9 | 3 | Human Resources | 1 | 184 | 3 | Male | 48 | 3 | 2 | Human Resources | 4 | Married | 6347 | 13982 | 0 | Y | Yes | 19 | 3 | 4 | 80 | 0 | 12 | 2 | 1 | 11 | 9 | 4 | 7 |
| 32 | Yes | Travel_Rarely | 1033 | Research & Development | 9 | 3 | Medical | 1 | 190 | 1 | Female | 41 | 3 | 1 | Laboratory Technician | 1 | Single | 4200 | 10224 | 7 | Y | No | 22 | 4 | 1 | 80 | 0 | 10 | 2 | 4 | 5 | 4 | 0 | 4 |
| 45 | No | Travel_Rarely | 1316 | Research & Development | 29 | 3 | Medical | 1 | 192 | 3 | Male | 83 | 3 | 1 | Research Scientist | 4 | Single | 3452 | 9752 | 5 | Y | No | 13 | 3 | 2 | 80 | 0 | 9 | 2 | 2 | 6 | 5 | 0 | 3 |
| NA | No | Travel_Rarely | 364 | Research & Development | 3 | 5 | Technical Degree | 1 | 193 | 4 | Female | 32 | 3 | 2 | Research Scientist | 3 | Single | 4317 | 2302 | 3 | Y | Yes | 20 | 4 | 2 | 80 | 0 | 19 | 2 | 3 | 3 | 2 | 2 | 2 |
| 30 | No | Travel_Rarely | 438 | Research & Development | 18 | 3 | Life Sciences | 1 | 194 | 1 | Female | 75 | 3 | 1 | Research Scientist | 3 | Single | 2632 | 23910 | 1 | Y | No | 14 | 3 | 3 | 80 | 0 | 5 | 4 | 2 | 5 | 4 | 0 | 4 |
| 32 | No | Travel_Frequently | 689 | Sales | 9 | 2 | Medical | 1 | 195 | 4 | Male | 35 | 1 | 2 | Sales Executive | 4 | Divorced | 4668 | 22812 | 0 | Y | No | 17 | 3 | 4 | 80 | 3 | 9 | 2 | 4 | 8 | 7 | 0 | 7 |
| 30 | No | Travel_Rarely | 201 | Research & Development | 5 | 3 | Technical Degree | 1 | 197 | 4 | Female | 84 | 3 | 1 | Research Scientist | 1 | Divorced | 3204 | 10415 | 5 | Y | No | 14 | 3 | 4 | 80 | 1 | 8 | 3 | 3 | 3 | 2 | 2 | 2 |
| 30 | No | Travel_Rarely | 1427 | Research & Development | 2 | 1 | Medical | 1 | 198 | 2 | Male | 35 | 2 | 1 | Laboratory Technician | 4 | Single | NA | 11162 | 0 | Y | No | 13 | 3 | 4 | 80 | 0 | 6 | 3 | 3 | 5 | 3 | 1 | 2 |
| 41 | No | Travel_Frequently | 857 | Research & Development | 10 | 3 | Life Sciences | 1 | 199 | 4 | Male | 91 | 2 | 4 | Manager | 1 | Divorced | 17181 | 12888 | 4 | Y | No | 13 | 3 | 2 | 80 | 1 | 21 | 2 | 2 | 7 | 6 | 7 | 7 |
| 41 | No | Travel_Rarely | 933 | Research & Development | 9 | 4 | Life Sciences | 1 | 200 | 3 | Male | 94 | 3 | 1 | Laboratory Technician | 1 | Married | NA | 6961 | 2 | Y | No | 21 | 4 | 4 | 80 | 1 | 7 | 2 | 3 | 5 | 0 | 1 | 4 |
| NA | No | Travel_Rarely | 1181 | Research & Development | 3 | 1 | Medical | 1 | 201 | 2 | Female | 79 | 3 | 1 | Laboratory Technician | 2 | Single | 1483 | 16102 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 40 | No | Travel_Frequently | 1395 | Research & Development | 26 | 3 | Medical | 1 | 202 | 2 | Female | 54 | 3 | 2 | Research Scientist | 2 | Divorced | NA | 8504 | 1 | Y | No | 11 | 3 | 1 | 80 | 1 | 20 | 2 | 3 | 20 | 7 | 2 | 13 |
| 35 | No | Travel_Rarely | 662 | Sales | 1 | 5 | Marketing | 1 | 204 | 3 | Male | 94 | 3 | 3 | Sales Executive | 2 | Married | 7295 | 11439 | 1 | Y | No | 13 | 3 | 1 | 80 | 2 | 10 | 3 | 3 | 10 | 8 | 0 | 6 |
| 53 | No | Travel_Rarely | 1436 | Sales | 6 | 2 | Marketing | 1 | 205 | 2 | Male | 34 | 3 | 2 | Sales Representative | 3 | Married | 2306 | 16047 | 2 | Y | Yes | 20 | 4 | 4 | 80 | 1 | 13 | 3 | 1 | 7 | 7 | 4 | 5 |
| 45 | No | Travel_Rarely | 194 | Research & Development | 9 | 3 | Life Sciences | 1 | 206 | 2 | Male | 60 | 3 | 2 | Laboratory Technician | 2 | Divorced | 2348 | 10901 | 8 | Y | No | 18 | 3 | 3 | 80 | 1 | 20 | 2 | 1 | 17 | 9 | 0 | 15 |
| NA | No | Travel_Frequently | 967 | Sales | 8 | 3 | Marketing | 1 | 207 | 2 | Female | 43 | 3 | 3 | Sales Executive | 4 | Single | 8998 | 15589 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 9 | 2 | 3 | 9 | 8 | 3 | 7 |
| 29 | No | Non-Travel | 1496 | Research & Development | 1 | 1 | Technical Degree | 1 | 208 | 4 | Male | 41 | 3 | 2 | Manufacturing Director | 3 | Married | 4319 | 26283 | 1 | Y | No | 13 | 3 | 1 | 80 | 1 | 10 | 1 | 3 | 10 | 7 | 0 | 9 |
| 51 | No | Travel_Rarely | 1169 | Research & Development | 7 | 4 | Medical | 1 | 211 | 2 | Male | 34 | 2 | 2 | Manufacturing Director | 3 | Married | 6132 | 13983 | 2 | Y | No | 17 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 1 | 0 | 0 | 0 |
| 58 | No | Travel_Rarely | 1145 | Research & Development | 9 | 3 | Medical | 1 | 214 | 2 | Female | 75 | 2 | 1 | Research Scientist | 2 | Married | 3346 | 11873 | 4 | Y | Yes | 20 | 4 | 2 | 80 | 1 | 9 | 3 | 2 | 1 | 0 | 0 | 0 |
| 40 | No | Travel_Rarely | 630 | Sales | 4 | 4 | Marketing | 1 | 215 | 3 | Male | 67 | 2 | 3 | Sales Executive | 4 | Married | 10855 | 8552 | 7 | Y | No | 11 | 3 | 1 | 80 | 1 | 15 | 2 | 2 | 12 | 11 | 2 | 11 |
| 34 | No | Travel_Frequently | 303 | Sales | 2 | 4 | Marketing | 1 | 216 | 3 | Female | 75 | 3 | 1 | Sales Representative | 3 | Married | 2231 | 11314 | 6 | Y | No | 18 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 4 | 3 | 1 | 2 |
| 22 | No | Travel_Rarely | 1256 | Research & Development | 19 | 1 | Medical | 1 | 217 | 3 | Male | 80 | 3 | 1 | Research Scientist | 4 | Married | 2323 | 11992 | 1 | Y | No | 24 | 4 | 1 | 80 | 2 | 2 | 6 | 3 | 2 | 2 | 2 | 2 |
| 27 | No | Non-Travel | 691 | Research & Development | 9 | 3 | Medical | 1 | 218 | 4 | Male | 57 | 3 | 1 | Research Scientist | 2 | Divorced | 2024 | 5970 | 6 | Y | No | 18 | 3 | 4 | 80 | 1 | 6 | 1 | 1 | 2 | 2 | 2 | 2 |
| 28 | No | Travel_Rarely | 440 | Research & Development | 21 | 3 | Medical | 1 | 221 | 3 | Male | 42 | 3 | 1 | Research Scientist | 4 | Married | 2713 | 6672 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 5 | 2 | 1 | 5 | 2 | 0 | 2 |
| 57 | No | Travel_Rarely | 334 | Research & Development | 24 | 2 | Life Sciences | 1 | 223 | 3 | Male | 83 | 4 | 3 | Healthcare Representative | 4 | Divorced | 9439 | 23402 | 3 | Y | Yes | 16 | 3 | 2 | 80 | 1 | 12 | 2 | 1 | 5 | 3 | 1 | 4 |
| 27 | NA | Non-Travel | 1450 | Research & Development | 3 | 3 | Medical | 1 | 224 | 3 | Male | 79 | 2 | 1 | Research Scientist | 3 | Divorced | 2566 | 25326 | 1 | Y | Yes | 15 | 3 | 4 | 80 | 1 | 1 | 2 | 2 | 1 | 1 | 0 | 1 |
| 50 | No | Travel_Rarely | 1452 | Research & Development | 11 | 3 | Life Sciences | 1 | 226 | 3 | Female | 53 | 3 | 5 | Manager | 2 | Single | 19926 | 17053 | 3 | Y | No | 15 | 3 | 2 | 80 | 0 | 21 | 5 | 3 | 5 | 4 | 4 | 4 |
| 41 | No | Travel_Rarely | 465 | Research & Development | 14 | 3 | Life Sciences | 1 | 227 | 1 | Male | 56 | 3 | 1 | Research Scientist | 3 | Divorced | 2451 | 4609 | 4 | Y | No | 12 | 3 | 1 | 80 | 1 | 13 | 2 | 3 | 9 | 8 | 1 | 8 |
| 30 | No | Travel_Rarely | 1339 | Sales | 5 | 3 | Life Sciences | 1 | 228 | 2 | Female | 41 | 3 | 3 | Sales Executive | 4 | Married | 9419 | 8053 | 2 | Y | No | 12 | 3 | 3 | 80 | 1 | 12 | 2 | 3 | 10 | 9 | 7 | 4 |
| 38 | No | Travel_Rarely | 702 | Sales | 1 | 4 | Life Sciences | 1 | 230 | 1 | Female | 59 | 2 | 2 | Sales Executive | 4 | Single | 8686 | 12930 | 4 | Y | No | 22 | 4 | 3 | 80 | 0 | 12 | 2 | 4 | 8 | 3 | 0 | 7 |
| 32 | No | Travel_Rarely | 120 | Research & Development | 6 | 5 | Life Sciences | 1 | 231 | 3 | Male | 43 | 3 | 1 | Research Scientist | 3 | Single | 3038 | 12430 | 3 | Y | No | 20 | 4 | 1 | 80 | 0 | 8 | 2 | 3 | 5 | 4 | 1 | 4 |
| 27 | No | Travel_Rarely | 1157 | Research & Development | 17 | 3 | Technical Degree | 1 | 233 | 3 | Male | 51 | 3 | 1 | Research Scientist | 2 | Married | 3058 | 13364 | 0 | Y | Yes | 16 | 3 | 4 | 80 | 1 | 6 | 3 | 2 | 5 | 2 | 1 | 1 |
| 19 | Yes | Travel_Frequently | 602 | Sales | 1 | 1 | Technical Degree | 1 | 235 | 3 | Female | 100 | 1 | 1 | Sales Representative | 1 | Single | 2325 | 20989 | 0 | Y | No | 21 | 4 | 1 | 80 | 0 | 1 | 5 | 4 | 0 | 0 | 0 | 0 |
| NA | No | Travel_Frequently | 1480 | Research & Development | 3 | 2 | Medical | 1 | 238 | 4 | Male | 30 | 3 | 1 | Laboratory Technician | 2 | Single | NA | 15062 | 4 | Y | No | 12 | 3 | 3 | 80 | 0 | 13 | 3 | 2 | 8 | 7 | 7 | 2 |
| 30 | No | Non-Travel | 111 | Research & Development | 9 | 3 | Medical | 1 | 239 | 3 | Male | 66 | 3 | 2 | Laboratory Technician | 1 | Divorced | 3072 | 11012 | 1 | Y | No | 11 | 3 | 3 | 80 | 2 | 12 | 4 | 3 | 12 | 9 | 6 | 10 |
| 45 | No | Travel_Rarely | 1268 | Sales | 4 | 2 | Life Sciences | 1 | 240 | 3 | Female | 30 | 3 | 2 | Sales Executive | 1 | Divorced | 5006 | 6319 | 4 | Y | Yes | 11 | 3 | 1 | 80 | 1 | 9 | 3 | 4 | 5 | 4 | 0 | 3 |
| 56 | No | Travel_Rarely | 713 | Research & Development | 8 | 3 | Life Sciences | 1 | 241 | 3 | Female | 67 | 3 | 1 | Research Scientist | 1 | Divorced | 4257 | 13939 | 4 | Y | Yes | 18 | 3 | 3 | 80 | 1 | 19 | 3 | 3 | 2 | 2 | 2 | 2 |
| 33 | No | Travel_Rarely | 134 | Research & Development | 2 | 3 | Life Sciences | 1 | 242 | 3 | Male | 90 | 3 | 1 | Research Scientist | 4 | Single | 2500 | 10515 | 0 | Y | No | 14 | 3 | 1 | 80 | 0 | 4 | 2 | 4 | 3 | 1 | 0 | 2 |
| 19 | Yes | Travel_Rarely | 303 | Research & Development | 2 | 3 | Life Sciences | 1 | 243 | 2 | Male | 47 | 2 | 1 | Laboratory Technician | 4 | Single | 1102 | 9241 | 1 | Y | No | 22 | 4 | 3 | 80 | 0 | 1 | 3 | 2 | 1 | 0 | 1 | 0 |
| 46 | No | Travel_Rarely | 526 | Sales | 1 | 2 | Marketing | 1 | 244 | 2 | Female | 92 | 3 | 3 | Sales Executive | 1 | Divorced | 10453 | 2137 | 1 | Y | No | 25 | 4 | 3 | 80 | 3 | 24 | 2 | 3 | 24 | 13 | 15 | 7 |
| 38 | No | Travel_Rarely | 1380 | Research & Development | 9 | 2 | Life Sciences | 1 | 245 | 3 | Female | 75 | 3 | 1 | Laboratory Technician | 4 | Single | 2288 | 6319 | 1 | Y | No | 12 | 3 | 3 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 2 | 1 |
| NA | No | Travel_Rarely | 140 | Research & Development | 12 | 1 | Medical | 1 | 246 | 3 | Female | 95 | 3 | 1 | Research Scientist | 4 | Married | 3929 | 6984 | 8 | Y | Yes | 23 | 4 | 3 | 80 | 1 | 7 | 0 | 3 | 4 | 2 | 0 | 2 |
| 34 | No | Travel_Rarely | 629 | Research & Development | 27 | 2 | Medical | 1 | 247 | 4 | Female | 95 | 3 | 1 | Research Scientist | 2 | Single | 2311 | 5711 | 2 | Y | No | 15 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 3 | 2 | 1 | 2 |
| 41 | Yes | Travel_Rarely | 1356 | Sales | 20 | 2 | Marketing | 1 | 248 | 2 | Female | 70 | 3 | 1 | Sales Representative | 2 | Single | 3140 | 21728 | 1 | Y | Yes | 22 | 4 | 4 | 80 | 0 | 4 | 5 | 2 | 4 | 3 | 0 | 2 |
| 50 | NA | Travel_Rarely | 328 | Research & Development | 1 | 3 | Medical | 1 | 249 | 3 | Male | 86 | 2 | 1 | Laboratory Technician | 3 | Married | 3690 | 3425 | 2 | Y | No | 15 | 3 | 4 | 80 | 1 | 5 | 2 | 2 | 3 | 2 | 0 | 2 |
| 53 | NA | Travel_Rarely | 1084 | Research & Development | 13 | 2 | Medical | 1 | 250 | 4 | Female | 57 | 4 | 2 | Manufacturing Director | 1 | Divorced | 4450 | 26250 | 1 | Y | No | 11 | 3 | 3 | 80 | 2 | 5 | 3 | 3 | 4 | 2 | 1 | 3 |
| 33 | No | Travel_Rarely | 931 | Research & Development | 14 | 3 | Medical | 1 | 252 | 4 | Female | 72 | 3 | 1 | Research Scientist | 2 | Married | 2756 | 4673 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 8 | 5 | 3 | 8 | 7 | 1 | 6 |
| 40 | No | Travel_Rarely | 989 | Research & Development | 4 | 1 | Medical | 1 | 253 | 4 | Female | 46 | 3 | 5 | Manager | 3 | Married | NA | 6499 | 1 | Y | No | 14 | 3 | 2 | 80 | 1 | 21 | 2 | 3 | 20 | 8 | 9 | 9 |
| 55 | No | Travel_Rarely | 692 | Research & Development | 14 | 4 | Medical | 1 | 254 | 3 | Male | 61 | 4 | 5 | Research Director | 2 | Single | 18722 | 13339 | 8 | Y | No | 11 | 3 | 4 | 80 | 0 | 36 | 3 | 3 | 24 | 15 | 2 | 15 |
| 34 | No | Travel_Frequently | 1069 | Research & Development | 2 | 1 | Life Sciences | 1 | 256 | 4 | Male | 45 | 2 | 2 | Manufacturing Director | 3 | Married | 9547 | 14074 | 1 | Y | No | 17 | 3 | 3 | 80 | 0 | 10 | 2 | 2 | 10 | 9 | 1 | 9 |
| 51 | No | Travel_Rarely | 313 | Research & Development | 3 | 3 | Medical | 1 | 258 | 4 | Female | 98 | 3 | 4 | Healthcare Representative | 2 | Single | 13734 | 7192 | 3 | Y | No | 18 | 3 | 3 | 80 | 0 | 21 | 6 | 3 | 7 | 7 | 1 | 0 |
| 52 | No | Travel_Rarely | 699 | Research & Development | 1 | 4 | Life Sciences | 1 | 259 | 3 | Male | 65 | 2 | 5 | Manager | 3 | Married | NA | 5678 | 0 | Y | No | 14 | 3 | 1 | 80 | 1 | 34 | 5 | 3 | 33 | 18 | 11 | 9 |
| 27 | No | Travel_Rarely | 894 | Research & Development | 9 | 3 | Medical | 1 | 260 | 4 | Female | 99 | 3 | 1 | Research Scientist | 2 | Single | 2279 | 11781 | 1 | Y | No | 16 | 3 | 4 | 80 | 0 | 7 | 2 | 2 | 7 | 7 | 0 | 3 |
| 35 | Yes | Travel_Rarely | 556 | Research & Development | 23 | 2 | Life Sciences | 1 | 261 | 2 | Male | 50 | 2 | 2 | Manufacturing Director | 3 | Married | 5916 | 15497 | 3 | Y | Yes | 13 | 3 | 1 | 80 | 0 | 8 | 1 | 3 | 1 | 0 | 0 | 1 |
| 43 | No | Non-Travel | 1344 | Research & Development | 7 | 3 | Medical | 1 | 262 | 4 | Male | 37 | 4 | 1 | Research Scientist | 4 | Divorced | 2089 | 5228 | 4 | Y | No | 14 | 3 | 4 | 80 | 3 | 7 | 3 | 4 | 5 | 4 | 2 | 2 |
| 45 | NA | Non-Travel | 1195 | Research & Development | 2 | 2 | Medical | 1 | 264 | 1 | Male | 65 | 2 | 4 | Manager | 4 | Married | 16792 | 20462 | 9 | Y | No | 23 | 4 | 4 | 80 | 1 | 22 | 1 | 3 | 20 | 8 | 11 | 8 |
| 37 | No | Travel_Rarely | 290 | Research & Development | 21 | 3 | Life Sciences | 1 | 267 | 2 | Male | 65 | 4 | 1 | Research Scientist | 1 | Married | 3564 | 22977 | 1 | Y | Yes | 12 | 3 | 1 | 80 | 1 | 8 | 3 | 2 | 8 | 7 | 1 | 7 |
| 35 | No | Travel_Frequently | 138 | Research & Development | 2 | 3 | Medical | 1 | 269 | 2 | Female | 37 | 3 | 2 | Laboratory Technician | 2 | Single | 4425 | 15986 | 5 | Y | No | 11 | 3 | 4 | 80 | 0 | 10 | 5 | 3 | 6 | 2 | 1 | 2 |
| NA | No | Non-Travel | 926 | Research & Development | 21 | 2 | Medical | 1 | 270 | 3 | Female | 36 | 3 | 2 | Manufacturing Director | 3 | Divorced | 5265 | 16439 | 2 | Y | No | 16 | 3 | 2 | 80 | 1 | 11 | 5 | 3 | 5 | 3 | 0 | 2 |
| 38 | No | Travel_Rarely | 1261 | Research & Development | 2 | 4 | Life Sciences | 1 | 271 | 4 | Male | 88 | 3 | 2 | Manufacturing Director | 3 | Married | 6553 | 7259 | 9 | Y | No | 14 | 3 | 2 | 80 | 0 | 14 | 3 | 3 | 1 | 0 | 0 | 0 |
| 38 | No | Travel_Rarely | 1084 | Research & Development | 29 | 3 | Technical Degree | 1 | 273 | 4 | Male | 54 | 3 | 2 | Manufacturing Director | 4 | Married | 6261 | 4185 | 3 | Y | No | 18 | 3 | 1 | 80 | 1 | 9 | 3 | 1 | 7 | 7 | 1 | 7 |
| 27 | No | Travel_Frequently | 472 | Research & Development | 1 | 1 | Technical Degree | 1 | 274 | 3 | Male | 60 | 2 | 2 | Manufacturing Director | 1 | Married | 4298 | 9679 | 5 | Y | No | 19 | 3 | 3 | 80 | 1 | 6 | 1 | 3 | 2 | 2 | 2 | 0 |
| 49 | No | Non-Travel | 1002 | Research & Development | 18 | 4 | Life Sciences | 1 | 275 | 4 | Male | 92 | 3 | 2 | Manufacturing Director | 4 | Divorced | 6804 | 23793 | 1 | Y | Yes | 15 | 3 | 1 | 80 | 2 | 7 | 0 | 3 | 7 | 7 | 1 | 7 |
| 34 | No | Travel_Frequently | 878 | Research & Development | 10 | 4 | Medical | 1 | 277 | 4 | Male | 43 | 3 | 1 | Research Scientist | 3 | Divorced | 3815 | 5972 | 1 | Y | Yes | 17 | 3 | 4 | 80 | 1 | 5 | 4 | 4 | 5 | 3 | 2 | 0 |
| 40 | No | Travel_Rarely | 905 | Research & Development | 19 | 2 | Medical | 1 | 281 | 3 | Male | 99 | 3 | 2 | Laboratory Technician | 4 | Married | NA | 16523 | 8 | Y | Yes | 15 | 3 | 3 | 80 | 1 | 15 | 2 | 4 | 7 | 2 | 3 | 7 |
| NA | Yes | Travel_Rarely | 1180 | Research & Development | 29 | 1 | Medical | 1 | 282 | 2 | Male | 70 | 3 | 2 | Healthcare Representative | 1 | Married | 6673 | 11354 | 7 | Y | Yes | 19 | 3 | 2 | 80 | 0 | 17 | 2 | 3 | 1 | 0 | 0 | 0 |
| 29 | Yes | Travel_Rarely | 121 | Sales | 27 | 3 | Marketing | 1 | 283 | 2 | Female | 35 | 3 | 3 | Sales Executive | 4 | Married | 7639 | 24525 | 1 | Y | No | 22 | 4 | 4 | 80 | 3 | 10 | 3 | 2 | 10 | 4 | 1 | 9 |
| 22 | No | Travel_Rarely | 1136 | Research & Development | 5 | 3 | Life Sciences | 1 | 284 | 4 | Male | 60 | 4 | 1 | Research Scientist | 2 | Divorced | NA | 12392 | 1 | Y | Yes | 16 | 3 | 1 | 80 | 1 | 4 | 2 | 2 | 4 | 2 | 2 | 2 |
| 36 | No | Travel_Frequently | 635 | Research & Development | 18 | 1 | Medical | 1 | 286 | 2 | Female | 73 | 3 | 1 | Laboratory Technician | 4 | Single | 2153 | 7703 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 8 | 2 | 3 | 8 | 1 | 1 | 7 |
| 40 | No | Non-Travel | 1151 | Research & Development | 9 | 5 | Life Sciences | 1 | 287 | 4 | Male | 63 | 2 | 2 | Healthcare Representative | 4 | Married | NA | 14242 | 9 | Y | No | 14 | 3 | 4 | 80 | 1 | 5 | 5 | 1 | 3 | 2 | 0 | 2 |
| 46 | No | Travel_Rarely | 644 | Research & Development | 1 | 4 | Medical | 1 | 288 | 4 | Male | 97 | 3 | 3 | Healthcare Representative | 1 | Divorced | NA | 12368 | 7 | Y | No | 16 | 3 | 3 | 80 | 1 | 17 | 3 | 3 | 4 | 2 | 0 | 3 |
| 32 | Yes | Travel_Rarely | 1045 | Sales | 4 | 4 | Medical | 1 | 291 | 4 | Male | 32 | 1 | 3 | Sales Executive | 4 | Married | 10400 | 25812 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 14 | 2 | 2 | 14 | 8 | 9 | 8 |
| NA | No | Non-Travel | 829 | Research & Development | 1 | 1 | Life Sciences | 1 | 292 | 3 | Male | 88 | 2 | 3 | Manufacturing Director | 3 | Single | 8474 | 20925 | 1 | Y | No | 22 | 4 | 3 | 80 | 0 | 12 | 2 | 3 | 11 | 8 | 5 | 8 |
| 27 | No | Travel_Frequently | 1242 | Sales | 20 | 3 | Life Sciences | 1 | 293 | 4 | Female | 90 | 3 | 2 | Sales Executive | 3 | Single | 9981 | 12916 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 7 | 2 | 3 | 7 | 7 | 0 | 7 |
| 51 | No | Travel_Rarely | 1469 | Research & Development | 8 | 4 | Life Sciences | 1 | 296 | 2 | Male | 81 | 2 | 3 | Research Director | 2 | Married | 12490 | 15736 | 5 | Y | No | 16 | 3 | 4 | 80 | 2 | 16 | 5 | 1 | 10 | 9 | 4 | 7 |
| 30 | Yes | Travel_Rarely | 1005 | Research & Development | 3 | 3 | Technical Degree | 1 | 297 | 4 | Female | 88 | 3 | 1 | Research Scientist | 1 | Single | 2657 | 8556 | 5 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 8 | 5 | 3 | 5 | 2 | 0 | 4 |
| 41 | No | Travel_Rarely | 896 | Sales | 6 | 3 | Life Sciences | 1 | 298 | 4 | Female | 75 | 3 | 3 | Manager | 4 | Single | 13591 | 14674 | 3 | Y | Yes | 18 | 3 | 3 | 80 | 0 | 16 | 3 | 3 | 1 | 0 | 0 | 0 |
| 30 | Yes | Travel_Frequently | 334 | Sales | 26 | 4 | Marketing | 1 | 299 | 3 | Female | 52 | 2 | 2 | Sales Executive | 1 | Single | 6696 | 22967 | 5 | Y | No | 15 | 3 | 3 | 80 | 0 | 9 | 5 | 2 | 6 | 3 | 0 | 1 |
| NA | Yes | Travel_Rarely | 992 | Research & Development | 1 | 3 | Technical Degree | 1 | 300 | 3 | Male | 85 | 3 | 1 | Research Scientist | 3 | Single | 2058 | 19757 | 0 | Y | No | 14 | 3 | 4 | 80 | 0 | 7 | 1 | 2 | 6 | 2 | 1 | 5 |
| 45 | No | Non-Travel | 1052 | Sales | 6 | 3 | Medical | 1 | 302 | 4 | Female | 57 | 2 | 3 | Sales Executive | 4 | Single | 8865 | 16840 | 6 | Y | No | 12 | 3 | 4 | 80 | 0 | 23 | 2 | 3 | 19 | 7 | 12 | 8 |
| 54 | NA | Travel_Rarely | 1147 | Sales | 3 | 3 | Marketing | 1 | 303 | 4 | Female | 52 | 3 | 2 | Sales Executive | 1 | Married | 5940 | 17011 | 2 | Y | No | 14 | 3 | 4 | 80 | 1 | 16 | 4 | 3 | 6 | 2 | 0 | 5 |
| 36 | No | Travel_Rarely | 1396 | Research & Development | 5 | 2 | Life Sciences | 1 | 304 | 4 | Male | 62 | 3 | 2 | Laboratory Technician | 2 | Single | 5914 | 9945 | 8 | Y | No | 16 | 3 | 4 | 80 | 0 | 16 | 3 | 4 | 13 | 11 | 3 | 7 |
| 33 | No | Travel_Rarely | 147 | Research & Development | 4 | 4 | Medical | 1 | 305 | 3 | Female | 47 | 2 | 1 | Research Scientist | 2 | Married | 2622 | 13248 | 6 | Y | No | 21 | 4 | 4 | 80 | 0 | 7 | 3 | 3 | 3 | 2 | 1 | 1 |
| 37 | No | Travel_Frequently | 663 | Research & Development | 11 | 3 | Other | 1 | 306 | 2 | Male | 47 | 3 | 3 | Research Director | 4 | Divorced | 12185 | 10056 | 1 | Y | Yes | 14 | 3 | 3 | 80 | 3 | 10 | 1 | 3 | 10 | 8 | 0 | 7 |
| 38 | No | Travel_Rarely | 119 | Sales | 3 | 3 | Life Sciences | 1 | 307 | 1 | Male | 76 | 3 | 3 | Sales Executive | 3 | Divorced | 10609 | 9647 | 0 | Y | No | 12 | 3 | 3 | 80 | 2 | 17 | 6 | 2 | 16 | 10 | 5 | 13 |
| 31 | No | Non-Travel | 979 | Research & Development | 1 | 4 | Medical | 1 | 308 | 3 | Male | 90 | 1 | 2 | Manufacturing Director | 3 | Married | 4345 | 4381 | 0 | Y | No | 12 | 3 | 4 | 80 | 1 | 6 | 2 | 3 | 5 | 4 | 1 | 4 |
| 59 | No | Travel_Rarely | 142 | Research & Development | 3 | 3 | Life Sciences | 1 | 309 | 3 | Male | 70 | 2 | 1 | Research Scientist | 4 | Married | 2177 | 8456 | 3 | Y | No | 17 | 3 | 1 | 80 | 1 | 7 | 6 | 3 | 1 | 0 | 0 | 0 |
| 37 | No | Travel_Frequently | 319 | Sales | 4 | 4 | Marketing | 1 | 311 | 1 | Male | 41 | 3 | 1 | Sales Representative | 4 | Divorced | 2793 | 2539 | 4 | Y | No | 17 | 3 | 3 | 80 | 1 | 13 | 2 | 3 | 9 | 8 | 5 | 8 |
| 29 | No | Travel_Frequently | 1413 | Sales | 1 | 1 | Medical | 1 | 312 | 2 | Female | 42 | 3 | 3 | Sales Executive | 4 | Married | 7918 | 6599 | 1 | Y | No | 14 | 3 | 4 | 80 | 1 | 11 | 5 | 3 | 11 | 10 | 4 | 1 |
| 35 | No | Travel_Frequently | 944 | Sales | 1 | 3 | Marketing | 1 | 314 | 3 | Female | 92 | 3 | 3 | Sales Executive | 3 | Single | 8789 | 9096 | 1 | Y | No | 14 | 3 | 1 | 80 | 0 | 10 | 3 | 4 | 10 | 7 | 0 | 8 |
| 29 | Yes | Travel_Rarely | 896 | Research & Development | 18 | 1 | Medical | 1 | 315 | 3 | Male | 86 | 2 | 1 | Research Scientist | 4 | Single | 2389 | 14961 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 4 | 3 | 2 | 4 | 3 | 0 | 1 |
| 52 | No | Travel_Rarely | 1323 | Research & Development | 2 | 3 | Life Sciences | 1 | 316 | 3 | Female | 89 | 2 | 1 | Laboratory Technician | 4 | Single | 3212 | 3300 | 7 | Y | No | 15 | 3 | 2 | 80 | 0 | 6 | 3 | 2 | 2 | 2 | 2 | 2 |
| 42 | No | Travel_Rarely | 532 | Research & Development | 4 | 2 | Technical Degree | 1 | 319 | 3 | Male | 58 | 3 | 5 | Manager | 4 | Married | 19232 | 4933 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 22 | 3 | 3 | 22 | 17 | 11 | 15 |
| 59 | No | Travel_Rarely | 818 | Human Resources | 6 | 2 | Medical | 1 | 321 | 2 | Male | 52 | 3 | 1 | Human Resources | 3 | Married | 2267 | 25657 | 8 | Y | No | 17 | 3 | 4 | 80 | 0 | 7 | 2 | 2 | 2 | 2 | 2 | 2 |
| 50 | No | Travel_Rarely | 854 | Sales | 1 | 4 | Medical | 1 | 323 | 4 | Female | 68 | 3 | 5 | Manager | 4 | Divorced | NA | 24118 | 3 | Y | No | 11 | 3 | 3 | 80 | 1 | 32 | 3 | 2 | 7 | 0 | 0 | 6 |
| 33 | Yes | Travel_Rarely | 813 | Research & Development | 14 | 3 | Medical | 1 | 325 | 3 | Male | 58 | 3 | 1 | Laboratory Technician | 4 | Married | 2436 | 22149 | 5 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 8 | 2 | 1 | 5 | 4 | 0 | 4 |
| 43 | No | Travel_Rarely | 1034 | Sales | 16 | 3 | Marketing | 1 | 327 | 4 | Female | 80 | 3 | 4 | Manager | 4 | Married | NA | 7744 | 5 | Y | Yes | 22 | 4 | 3 | 80 | 1 | 22 | 3 | 3 | 17 | 13 | 1 | 9 |
| 33 | Yes | Travel_Rarely | 465 | Research & Development | 2 | 2 | Life Sciences | 1 | 328 | 1 | Female | 39 | 3 | 1 | Laboratory Technician | 1 | Married | 2707 | 21509 | 7 | Y | No | 20 | 4 | 1 | 80 | 0 | 13 | 3 | 4 | 9 | 7 | 1 | 7 |
| 52 | No | Non-Travel | 771 | Sales | 2 | 4 | Life Sciences | 1 | 329 | 1 | Male | 79 | 2 | 5 | Manager | 3 | Single | 19068 | 21030 | 1 | Y | Yes | 18 | 3 | 4 | 80 | 0 | 33 | 2 | 4 | 33 | 7 | 15 | 12 |
| 32 | No | Travel_Rarely | 1401 | Sales | 4 | 2 | Life Sciences | 1 | 330 | 3 | Female | 56 | 3 | 1 | Sales Representative | 2 | Married | NA | 20990 | 2 | Y | No | 11 | 3 | 1 | 80 | 1 | 6 | 5 | 3 | 4 | 3 | 1 | 2 |
| 32 | Yes | Travel_Rarely | 515 | Research & Development | 1 | 3 | Life Sciences | 1 | 331 | 4 | Male | 62 | 2 | 1 | Laboratory Technician | 3 | Single | 3730 | 9571 | 0 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 4 | 2 | 1 | 3 | 2 | 1 | 2 |
| 39 | No | Travel_Rarely | 1431 | Research & Development | 1 | 4 | Medical | 1 | 332 | 3 | Female | 96 | 3 | 1 | Laboratory Technician | 3 | Divorced | 2232 | 15417 | 7 | Y | No | 14 | 3 | 3 | 80 | 3 | 7 | 1 | 3 | 3 | 2 | 1 | 2 |
| NA | No | Non-Travel | 976 | Sales | 26 | 4 | Marketing | 1 | 333 | 3 | Male | 100 | 3 | 2 | Sales Executive | 4 | Married | 4465 | 12069 | 0 | Y | No | 18 | 3 | 1 | 80 | 0 | 4 | 2 | 3 | 3 | 2 | 2 | 2 |
| 41 | No | Travel_Rarely | 1411 | Research & Development | 19 | 2 | Life Sciences | 1 | 334 | 3 | Male | 36 | 3 | 2 | Research Scientist | 1 | Divorced | 3072 | 19877 | 2 | Y | No | 16 | 3 | 1 | 80 | 2 | 17 | 2 | 2 | 1 | 0 | 0 | 0 |
| 40 | No | Travel_Rarely | 1300 | Research & Development | 24 | 2 | Technical Degree | 1 | 335 | 1 | Male | 62 | 3 | 2 | Research Scientist | 4 | Divorced | 3319 | 24447 | 1 | Y | No | 17 | 3 | 1 | 80 | 2 | 9 | 3 | 3 | 9 | 8 | 4 | 7 |
| 45 | No | Travel_Rarely | 252 | Research & Development | 1 | 3 | Other | 1 | 336 | 3 | Male | 70 | 4 | 5 | Manager | 4 | Married | 19202 | 15970 | 0 | Y | No | 11 | 3 | 3 | 80 | 1 | 25 | 2 | 3 | 24 | 0 | 1 | 7 |
| 31 | No | Travel_Frequently | 1327 | Research & Development | 3 | 4 | Medical | 1 | 337 | 2 | Male | 73 | 3 | 3 | Research Director | 3 | Divorced | 13675 | 13523 | 9 | Y | No | 12 | 3 | 1 | 80 | 1 | 9 | 3 | 3 | 2 | 2 | 2 | 2 |
| 33 | No | Travel_Rarely | 832 | Research & Development | 5 | 4 | Life Sciences | 1 | 338 | 3 | Female | 63 | 2 | 1 | Research Scientist | 4 | Married | NA | 14776 | 1 | Y | No | 13 | 3 | 3 | 80 | 1 | 2 | 2 | 2 | 2 | 2 | 0 | 2 |
| 34 | No | Travel_Rarely | 470 | Research & Development | 2 | 4 | Life Sciences | 1 | 339 | 4 | Male | 84 | 2 | 2 | Manufacturing Director | 1 | Married | 5957 | 23687 | 6 | Y | No | 13 | 3 | 2 | 80 | 1 | 13 | 3 | 3 | 11 | 9 | 5 | 9 |
| 37 | No | Travel_Rarely | 1017 | Research & Development | 1 | 2 | Medical | 1 | 340 | 3 | Female | 83 | 2 | 1 | Research Scientist | 1 | Married | 3920 | 18697 | 2 | Y | No | 14 | 3 | 1 | 80 | 1 | 17 | 2 | 2 | 3 | 1 | 0 | 2 |
| 45 | No | Travel_Frequently | 1199 | Research & Development | 7 | 4 | Life Sciences | 1 | 341 | 1 | Male | 77 | 4 | 2 | Manufacturing Director | 3 | Married | 6434 | 5118 | 4 | Y | No | 17 | 3 | 4 | 80 | 1 | 9 | 1 | 3 | 3 | 2 | 0 | 2 |
| 37 | Yes | Travel_Frequently | 504 | Research & Development | 10 | 3 | Medical | 1 | 342 | 1 | Male | 61 | 3 | 3 | Manufacturing Director | 3 | Divorced | 10048 | 22573 | 6 | Y | No | 11 | 3 | 2 | 80 | 2 | 17 | 5 | 3 | 1 | 0 | 0 | 0 |
| 39 | No | Travel_Frequently | 505 | Research & Development | 2 | 4 | Technical Degree | 1 | 343 | 3 | Female | 64 | 3 | 3 | Healthcare Representative | 3 | Single | 10938 | 6420 | 0 | Y | No | 25 | 4 | 4 | 80 | 0 | 20 | 1 | 3 | 19 | 6 | 11 | 8 |
| 29 | No | Travel_Rarely | 665 | Research & Development | 15 | 3 | Life Sciences | 1 | 346 | 3 | Male | 60 | 3 | 1 | Research Scientist | 4 | Single | 2340 | 22673 | 1 | Y | No | 19 | 3 | 1 | 80 | 0 | 6 | 1 | 3 | 6 | 5 | 1 | 5 |
| 42 | No | Travel_Rarely | 916 | Research & Development | 17 | 2 | Life Sciences | 1 | 347 | 4 | Female | 82 | 4 | 2 | Research Scientist | 1 | Single | 6545 | 23016 | 3 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 10 | 1 | 3 | 3 | 2 | 0 | 2 |
| 29 | No | Travel_Rarely | 1247 | Sales | 20 | 2 | Marketing | 1 | 349 | 4 | Male | 45 | 3 | 2 | Sales Executive | 4 | Divorced | 6931 | 10732 | 2 | Y | No | 14 | 3 | 4 | 80 | 1 | 10 | 2 | 3 | 3 | 2 | 0 | 2 |
| 25 | No | Travel_Rarely | 685 | Research & Development | 1 | 3 | Life Sciences | 1 | 350 | 1 | Female | 62 | 3 | 2 | Manufacturing Director | 3 | Married | 4898 | 7505 | 0 | Y | No | 12 | 3 | 4 | 80 | 2 | 5 | 3 | 3 | 4 | 2 | 1 | 2 |
| 42 | No | Travel_Rarely | 269 | Research & Development | 2 | 3 | Medical | 1 | 351 | 4 | Female | 56 | 2 | 1 | Laboratory Technician | 1 | Divorced | 2593 | 8007 | 0 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 10 | 4 | 3 | 9 | 6 | 7 | 8 |
| 40 | No | Travel_Rarely | 1416 | Research & Development | 2 | 2 | Medical | 1 | 352 | 1 | Male | 49 | 3 | 5 | Research Director | 3 | Divorced | 19436 | 5949 | 0 | Y | No | 19 | 3 | 4 | 80 | 1 | 22 | 5 | 3 | 21 | 7 | 3 | 9 |
| NA | No | Travel_Rarely | 833 | Research & Development | 1 | 3 | Life Sciences | 1 | 353 | 3 | Male | 96 | 3 | 1 | Research Scientist | 4 | Married | 2723 | 23231 | 1 | Y | No | 11 | 3 | 2 | 80 | 0 | 1 | 0 | 2 | 1 | 0 | 0 | 0 |
| 31 | Yes | Travel_Frequently | 307 | Research & Development | 29 | 2 | Medical | 1 | 355 | 3 | Male | 71 | 2 | 1 | Laboratory Technician | 2 | Single | 3479 | 11652 | 0 | Y | No | 11 | 3 | 2 | 80 | 0 | 6 | 2 | 4 | 5 | 4 | 1 | 4 |
| 32 | NA | Travel_Frequently | 1311 | Research & Development | 7 | 3 | Life Sciences | 1 | 359 | 2 | Male | 100 | 4 | 1 | Laboratory Technician | 2 | Married | 2794 | 26062 | 1 | Y | No | 20 | 4 | 3 | 80 | 0 | 5 | 3 | 1 | 5 | 1 | 0 | 3 |
| 38 | No | Non-Travel | 1327 | Sales | 2 | 2 | Life Sciences | 1 | 361 | 4 | Male | 39 | 2 | 2 | Sales Executive | 4 | Married | 5249 | 19682 | 3 | Y | No | 18 | 3 | 4 | 80 | 1 | 13 | 0 | 3 | 8 | 7 | 7 | 5 |
| 32 | No | Travel_Rarely | 128 | Research & Development | 2 | 1 | Technical Degree | 1 | 362 | 4 | Male | 84 | 2 | 2 | Laboratory Technician | 1 | Single | 2176 | 19737 | 4 | Y | No | 13 | 3 | 4 | 80 | 0 | 9 | 5 | 3 | 6 | 2 | 0 | 4 |
| 46 | No | Travel_Rarely | 488 | Sales | 2 | 3 | Technical Degree | 1 | 363 | 3 | Female | 75 | 1 | 4 | Manager | 2 | Married | 16872 | 14977 | 3 | Y | Yes | 12 | 3 | 2 | 80 | 1 | 28 | 2 | 2 | 7 | 7 | 7 | 7 |
| 28 | Yes | Travel_Rarely | 529 | Research & Development | 2 | 4 | Life Sciences | 1 | 364 | 1 | Male | 79 | 3 | 1 | Laboratory Technician | 3 | Single | 3485 | 14935 | 2 | Y | No | 11 | 3 | 3 | 80 | 0 | 5 | 5 | 1 | 0 | 0 | 0 | 0 |
| 29 | No | Travel_Rarely | 1210 | Sales | 2 | 3 | Medical | 1 | 366 | 1 | Male | 78 | 2 | 2 | Sales Executive | 2 | Married | 6644 | 3687 | 2 | Y | No | 19 | 3 | 2 | 80 | 2 | 10 | 2 | 3 | 0 | 0 | 0 | 0 |
| 31 | No | Travel_Rarely | 1463 | Research & Development | 23 | 3 | Medical | 1 | 367 | 2 | Male | 64 | 2 | 2 | Healthcare Representative | 4 | Married | 5582 | 14408 | 0 | Y | No | 21 | 4 | 2 | 80 | 1 | 10 | 2 | 3 | 9 | 0 | 7 | 8 |
| 25 | No | Non-Travel | 675 | Research & Development | 5 | 2 | Life Sciences | 1 | 369 | 2 | Male | 85 | 4 | 2 | Healthcare Representative | 1 | Divorced | 4000 | 18384 | 1 | Y | No | 12 | 3 | 4 | 80 | 2 | 6 | 2 | 3 | 6 | 3 | 1 | 5 |
| 45 | No | Travel_Rarely | 1385 | Research & Development | 20 | 2 | Medical | 1 | 372 | 3 | Male | 79 | 3 | 4 | Healthcare Representative | 4 | Married | 13496 | 7501 | 0 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 21 | 2 | 3 | 20 | 7 | 4 | 10 |
| 36 | No | Travel_Rarely | 1403 | Research & Development | 6 | 3 | Life Sciences | 1 | 373 | 4 | Male | 47 | 3 | 1 | Laboratory Technician | 4 | Married | 3210 | 20251 | 0 | Y | No | 11 | 3 | 3 | 80 | 1 | 16 | 4 | 3 | 15 | 13 | 10 | 11 |
| 55 | No | Travel_Rarely | 452 | Research & Development | 1 | 3 | Medical | 1 | 374 | 4 | Male | 81 | 3 | 5 | Manager | 1 | Single | 19045 | 18938 | 0 | Y | Yes | 14 | 3 | 3 | 80 | 0 | 37 | 2 | 3 | 36 | 10 | 4 | 13 |
| 47 | Yes | Non-Travel | 666 | Research & Development | 29 | 4 | Life Sciences | 1 | 376 | 1 | Male | 88 | 3 | 3 | Manager | 2 | Married | 11849 | 10268 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 1 | 10 | 2 | 2 | 10 | 7 | 9 | 9 |
| 28 | No | Travel_Rarely | 1158 | Research & Development | 9 | 3 | Medical | 1 | 377 | 4 | Male | 94 | 3 | 1 | Research Scientist | 4 | Married | 2070 | 2613 | 1 | Y | No | 23 | 4 | 4 | 80 | 1 | 5 | 3 | 2 | 5 | 2 | 0 | 4 |
| 37 | No | Travel_Rarely | 228 | Sales | 6 | 4 | Medical | 1 | 378 | 3 | Male | 98 | 3 | 2 | Sales Executive | 4 | Married | 6502 | 22825 | 4 | Y | No | 14 | 3 | 2 | 80 | 1 | 7 | 5 | 4 | 5 | 4 | 0 | 1 |
| 21 | No | Travel_Rarely | 996 | Research & Development | 3 | 2 | Medical | 1 | 379 | 4 | Male | 100 | 2 | 1 | Research Scientist | 3 | Single | 3230 | 10531 | 1 | Y | No | 17 | 3 | 1 | 80 | 0 | 3 | 4 | 4 | 3 | 2 | 1 | 0 |
| 37 | No | Non-Travel | 728 | Research & Development | 1 | 4 | Medical | 1 | 380 | 1 | Female | 80 | 3 | 3 | Research Director | 4 | Divorced | 13603 | 11677 | 2 | Y | Yes | 18 | 3 | 1 | 80 | 2 | 15 | 2 | 3 | 5 | 2 | 0 | 2 |
| 35 | No | Travel_Rarely | 1315 | Research & Development | 22 | 3 | Life Sciences | 1 | 381 | 2 | Female | 71 | 4 | 3 | Manager | 2 | Divorced | 11996 | 19100 | 7 | Y | No | 18 | 3 | 2 | 80 | 1 | 10 | 6 | 2 | 7 | 7 | 6 | 2 |
| 38 | No | Travel_Rarely | 322 | Sales | 7 | 2 | Medical | 1 | 382 | 1 | Female | 44 | 4 | 2 | Sales Executive | 1 | Divorced | 5605 | 19191 | 1 | Y | Yes | 24 | 4 | 3 | 80 | 1 | 8 | 3 | 3 | 8 | 0 | 7 | 7 |
| 26 | No | Travel_Frequently | 1479 | Research & Development | 1 | 3 | Life Sciences | 1 | 384 | 3 | Female | 84 | 3 | 2 | Manufacturing Director | 2 | Divorced | NA | 26767 | 1 | Y | No | 20 | 4 | 1 | 80 | 1 | 6 | 6 | 1 | 6 | 5 | 1 | 4 |
| 50 | No | Travel_Rarely | 797 | Research & Development | 4 | 1 | Life Sciences | 1 | 385 | 1 | Male | 96 | 3 | 5 | Research Director | 2 | Divorced | 19144 | 15815 | 3 | Y | No | 14 | 3 | 1 | 80 | 2 | 28 | 4 | 2 | 10 | 4 | 1 | 6 |
| 53 | No | Travel_Rarely | 1070 | Research & Development | 3 | 4 | Medical | 1 | 386 | 3 | Male | 45 | 3 | 4 | Research Director | 3 | Married | NA | 21016 | 3 | Y | Yes | 16 | 3 | 4 | 80 | 3 | 21 | 5 | 2 | 5 | 3 | 1 | 3 |
| 42 | No | Travel_Rarely | 635 | Sales | 1 | 1 | Life Sciences | 1 | 387 | 2 | Male | 99 | 3 | 2 | Sales Executive | 3 | Married | 4907 | 24532 | 1 | Y | No | 25 | 4 | 3 | 80 | 0 | 20 | 3 | 3 | 20 | 16 | 11 | 6 |
| 29 | No | Travel_Frequently | 442 | Sales | 2 | 2 | Life Sciences | 1 | 388 | 2 | Male | 44 | 3 | 2 | Sales Executive | 4 | Single | 4554 | 20260 | 1 | Y | No | 18 | 3 | 1 | 80 | 0 | 10 | 3 | 2 | 10 | 7 | 0 | 9 |
| 55 | No | Travel_Rarely | 147 | Research & Development | 20 | 2 | Technical Degree | 1 | 389 | 2 | Male | 37 | 3 | 2 | Laboratory Technician | 4 | Married | 5415 | 15972 | 3 | Y | Yes | 19 | 3 | 4 | 80 | 1 | 12 | 4 | 3 | 10 | 7 | 0 | 8 |
| 26 | No | Travel_Frequently | 496 | Research & Development | 11 | 2 | Medical | 1 | 390 | 1 | Male | 60 | 3 | 2 | Healthcare Representative | 1 | Married | 4741 | 22722 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 5 | 3 | 3 | 5 | 3 | 3 | 3 |
| 37 | No | Travel_Rarely | 1372 | Research & Development | 1 | 3 | Life Sciences | 1 | 391 | 4 | Female | 42 | 3 | 1 | Research Scientist | 4 | Single | 2115 | 15881 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 17 | 3 | 3 | 17 | 12 | 5 | 7 |
| 44 | Yes | Travel_Frequently | 920 | Research & Development | 24 | 3 | Life Sciences | 1 | 392 | 4 | Male | 43 | 3 | 1 | Laboratory Technician | 3 | Divorced | 3161 | 19920 | 3 | Y | Yes | 22 | 4 | 4 | 80 | 1 | 19 | 0 | 1 | 1 | 0 | 0 | 0 |
| 38 | No | Travel_Rarely | 688 | Research & Development | 23 | 4 | Life Sciences | 1 | 393 | 4 | Male | 82 | 3 | 2 | Healthcare Representative | 4 | Divorced | 5745 | 18899 | 9 | Y | No | 14 | 3 | 2 | 80 | 1 | 10 | 2 | 3 | 2 | 2 | 1 | 2 |
| 26 | Yes | Travel_Rarely | 1449 | Research & Development | 16 | 4 | Medical | 1 | 394 | 1 | Male | 45 | 3 | 1 | Laboratory Technician | 2 | Divorced | 2373 | 14180 | 2 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 5 | 2 | 3 | 3 | 2 | 0 | 2 |
| 28 | No | Travel_Rarely | 1117 | Research & Development | 8 | 2 | Life Sciences | 1 | 395 | 4 | Female | 66 | 3 | 1 | Research Scientist | 4 | Single | 3310 | 4488 | 1 | Y | No | 21 | 4 | 4 | 80 | 0 | 5 | 3 | 3 | 5 | 3 | 0 | 2 |
| 49 | No | Travel_Frequently | 636 | Research & Development | 10 | 4 | Life Sciences | 1 | 396 | 3 | Female | 35 | 3 | 5 | Research Director | 1 | Single | NA | 25594 | 9 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 22 | 4 | 3 | 3 | 2 | 1 | 2 |
| 36 | No | Travel_Rarely | 506 | Research & Development | 3 | 3 | Technical Degree | 1 | 397 | 3 | Male | 30 | 3 | 2 | Research Scientist | 2 | Single | 4485 | 26285 | 4 | Y | No | 12 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 8 | 0 | 7 | 7 |
| NA | No | Travel_Frequently | 444 | Sales | 5 | 3 | Marketing | 1 | 399 | 4 | Female | 84 | 3 | 1 | Sales Representative | 2 | Divorced | 2789 | 3909 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 2 | 5 | 2 | 2 | 2 | 2 | 2 |
| 26 | NA | Travel_Rarely | 950 | Sales | 4 | 4 | Marketing | 1 | 401 | 4 | Male | 48 | 2 | 2 | Sales Executive | 4 | Single | 5828 | 8450 | 1 | Y | Yes | 12 | 3 | 2 | 80 | 0 | 8 | 0 | 3 | 8 | 7 | 7 | 4 |
| 37 | No | Travel_Frequently | 889 | Research & Development | 9 | 3 | Medical | 1 | 403 | 2 | Male | 53 | 3 | 1 | Research Scientist | 4 | Married | 2326 | 11411 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 3 | 4 | 3 | 2 | 4 | 2 | 1 | 2 |
| 42 | No | Travel_Frequently | 555 | Sales | 26 | 3 | Marketing | 1 | 404 | 3 | Female | 77 | 3 | 4 | Sales Executive | 2 | Married | NA | 14864 | 5 | Y | No | 14 | 3 | 4 | 80 | 1 | 23 | 2 | 4 | 20 | 4 | 4 | 8 |
| 18 | Yes | Travel_Rarely | 230 | Research & Development | 3 | 3 | Life Sciences | 1 | 405 | 3 | Male | 54 | 3 | 1 | Laboratory Technician | 3 | Single | 1420 | 25233 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 |
| 35 | No | Travel_Rarely | 1232 | Sales | 16 | 3 | Marketing | 1 | 406 | 3 | Male | 96 | 3 | 3 | Sales Executive | 2 | Married | 8020 | 5100 | 0 | Y | No | 15 | 3 | 3 | 80 | 2 | 12 | 3 | 2 | 11 | 9 | 6 | 9 |
| 36 | No | Travel_Frequently | 566 | Research & Development | 18 | 4 | Life Sciences | 1 | 407 | 3 | Male | 81 | 4 | 1 | Laboratory Technician | 4 | Married | 3688 | 7122 | 4 | Y | No | 18 | 3 | 4 | 80 | 2 | 4 | 2 | 3 | 1 | 0 | 0 | 0 |
| 51 | No | Travel_Rarely | 1302 | Research & Development | 2 | 3 | Medical | 1 | 408 | 4 | Male | 84 | 1 | 2 | Manufacturing Director | 2 | Divorced | NA | 16321 | 5 | Y | No | 18 | 3 | 4 | 80 | 1 | 13 | 3 | 3 | 4 | 1 | 1 | 2 |
| 41 | No | Travel_Rarely | 334 | Sales | 2 | 4 | Life Sciences | 1 | 410 | 4 | Male | 88 | 3 | 4 | Manager | 2 | Single | 16015 | 15896 | 1 | Y | No | 19 | 3 | 2 | 80 | 0 | 22 | 2 | 3 | 22 | 10 | 0 | 4 |
| 18 | No | Travel_Rarely | 812 | Sales | 10 | 3 | Medical | 1 | 411 | 4 | Female | 69 | 2 | 1 | Sales Representative | 3 | Single | NA | 9724 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 |
| 28 | No | Travel_Rarely | 1476 | Research & Development | 16 | 2 | Medical | 1 | 412 | 2 | Male | 68 | 4 | 2 | Healthcare Representative | 1 | Single | 5661 | 4824 | 0 | Y | No | 19 | 3 | 3 | 80 | 0 | 9 | 2 | 3 | 8 | 3 | 0 | 7 |
| 31 | No | Travel_Rarely | 218 | Sales | 7 | 3 | Technical Degree | 1 | 416 | 2 | Male | 100 | 4 | 2 | Sales Executive | 4 | Married | 6929 | 12241 | 4 | Y | No | 11 | 3 | 2 | 80 | 1 | 10 | 3 | 2 | 8 | 7 | 7 | 7 |
| 39 | No | Travel_Rarely | 1132 | Research & Development | 1 | 3 | Medical | 1 | 417 | 3 | Male | 48 | 4 | 3 | Healthcare Representative | 4 | Divorced | 9613 | 10942 | 0 | Y | No | 17 | 3 | 1 | 80 | 3 | 19 | 5 | 2 | 18 | 10 | 3 | 7 |
| 36 | No | Non-Travel | 1105 | Research & Development | 24 | 4 | Life Sciences | 1 | 419 | 2 | Female | 47 | 3 | 2 | Laboratory Technician | 2 | Married | 5674 | 6927 | 7 | Y | No | 15 | 3 | 3 | 80 | 1 | 11 | 3 | 3 | 9 | 8 | 0 | 8 |
| 32 | No | Travel_Rarely | 906 | Sales | 7 | 3 | Life Sciences | 1 | 420 | 4 | Male | 91 | 2 | 2 | Sales Executive | 3 | Married | 5484 | 16985 | 1 | Y | No | 14 | 3 | 3 | 80 | 1 | 13 | 3 | 2 | 13 | 8 | 4 | 8 |
| NA | No | Travel_Rarely | 849 | Research & Development | 25 | 2 | Life Sciences | 1 | 421 | 1 | Female | 81 | 2 | 3 | Research Director | 2 | Married | NA | 26707 | 3 | Y | No | 17 | 3 | 3 | 80 | 1 | 19 | 2 | 3 | 10 | 8 | 0 | 1 |
| 58 | No | Non-Travel | 390 | Research & Development | 1 | 4 | Life Sciences | 1 | 422 | 4 | Male | 32 | 1 | 2 | Healthcare Representative | 3 | Divorced | NA | 17056 | 2 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 12 | 2 | 3 | 5 | 3 | 1 | 2 |
| 31 | NA | Travel_Rarely | 691 | Research & Development | 5 | 4 | Technical Degree | 1 | 423 | 3 | Male | 86 | 3 | 1 | Research Scientist | 4 | Married | 4821 | 10077 | 0 | Y | Yes | 12 | 3 | 3 | 80 | 1 | 6 | 4 | 3 | 5 | 2 | 0 | 3 |
| NA | No | Travel_Rarely | 106 | Human Resources | 2 | 3 | Human Resources | 1 | 424 | 1 | Male | 62 | 2 | 2 | Human Resources | 1 | Married | 6410 | 17822 | 3 | Y | No | 12 | 3 | 4 | 80 | 0 | 9 | 1 | 3 | 2 | 2 | 1 | 0 |
| 45 | No | Travel_Frequently | 1249 | Research & Development | 7 | 3 | Life Sciences | 1 | 425 | 1 | Male | 97 | 3 | 3 | Laboratory Technician | 1 | Divorced | 5210 | 20308 | 1 | Y | No | 18 | 3 | 1 | 80 | 1 | 24 | 2 | 3 | 24 | 9 | 9 | 11 |
| 31 | NA | Travel_Rarely | 192 | Research & Development | 2 | 4 | Life Sciences | 1 | 426 | 3 | Male | 32 | 3 | 1 | Research Scientist | 4 | Divorced | 2695 | 7747 | 0 | Y | Yes | 18 | 3 | 2 | 80 | 1 | 3 | 2 | 1 | 2 | 2 | 2 | 2 |
| 33 | No | Travel_Frequently | 553 | Research & Development | 5 | 4 | Life Sciences | 1 | 428 | 4 | Female | 74 | 3 | 3 | Manager | 2 | Married | 11878 | 23364 | 6 | Y | No | 11 | 3 | 2 | 80 | 2 | 12 | 2 | 3 | 10 | 6 | 8 | 8 |
| 39 | No | Travel_Rarely | 117 | Research & Development | 10 | 1 | Medical | 1 | 429 | 3 | Male | 99 | 3 | 4 | Manager | 1 | Married | 17068 | 5355 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 21 | 3 | 3 | 21 | 9 | 11 | 10 |
| 43 | No | Travel_Frequently | 185 | Research & Development | 10 | 4 | Life Sciences | 1 | 430 | 3 | Female | 33 | 3 | 1 | Laboratory Technician | 4 | Single | 2455 | 10675 | 0 | Y | No | 19 | 3 | 1 | 80 | 0 | 9 | 5 | 3 | 8 | 7 | 1 | 7 |
| 49 | No | Travel_Rarely | 1091 | Research & Development | 1 | 2 | Technical Degree | 1 | 431 | 3 | Female | 90 | 2 | 4 | Healthcare Representative | 3 | Single | 13964 | 17810 | 7 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 25 | 2 | 3 | 7 | 1 | 0 | 7 |
| 52 | NA | Travel_Rarely | 723 | Research & Development | 8 | 4 | Medical | 1 | 433 | 3 | Male | 85 | 2 | 2 | Research Scientist | 2 | Married | 4941 | 17747 | 2 | Y | No | 15 | 3 | 1 | 80 | 0 | 11 | 3 | 2 | 8 | 2 | 7 | 7 |
| 27 | No | Travel_Rarely | 1220 | Research & Development | 5 | 3 | Life Sciences | 1 | 434 | 3 | Female | 85 | 3 | 1 | Research Scientist | 2 | Single | 2478 | 20938 | 1 | Y | Yes | 12 | 3 | 2 | 80 | 0 | 4 | 2 | 2 | 4 | 3 | 1 | 2 |
| 32 | No | Travel_Rarely | 588 | Sales | 8 | 2 | Technical Degree | 1 | 436 | 3 | Female | 65 | 2 | 2 | Sales Executive | 2 | Married | 5228 | 24624 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 13 | 2 | 3 | 13 | 12 | 11 | 9 |
| 27 | No | Travel_Rarely | 1377 | Sales | 2 | 3 | Life Sciences | 1 | 437 | 4 | Male | 74 | 3 | 2 | Sales Executive | 3 | Single | 4478 | 5242 | 1 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 5 | 3 | 3 | 5 | 4 | 0 | 4 |
| 31 | No | Travel_Rarely | 691 | Sales | 7 | 3 | Marketing | 1 | 438 | 4 | Male | 73 | 3 | 2 | Sales Executive | 4 | Divorced | 7547 | 7143 | 4 | Y | No | 12 | 3 | 4 | 80 | 3 | 13 | 3 | 3 | 7 | 7 | 1 | 7 |
| 32 | No | Travel_Rarely | 1018 | Research & Development | 2 | 4 | Medical | 1 | 439 | 1 | Female | 74 | 4 | 2 | Research Scientist | 4 | Single | 5055 | 10557 | 7 | Y | No | 16 | 3 | 3 | 80 | 0 | 10 | 0 | 2 | 7 | 7 | 0 | 7 |
| 28 | Yes | Travel_Rarely | 1157 | Research & Development | 2 | 4 | Medical | 1 | 440 | 1 | Male | 84 | 1 | 1 | Research Scientist | 4 | Married | 3464 | 24737 | 5 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 5 | 4 | 2 | 3 | 2 | 2 | 2 |
| 30 | No | Travel_Rarely | 1275 | Research & Development | 28 | 2 | Medical | 1 | 441 | 4 | Female | 64 | 3 | 2 | Research Scientist | 4 | Married | 5775 | 11934 | 1 | Y | No | 13 | 3 | 4 | 80 | 2 | 11 | 2 | 3 | 10 | 8 | 1 | 9 |
| 31 | No | Travel_Frequently | 798 | Research & Development | 7 | 2 | Life Sciences | 1 | 442 | 3 | Female | 48 | 2 | 3 | Manufacturing Director | 3 | Married | 8943 | 14034 | 1 | Y | No | 24 | 4 | 1 | 80 | 1 | 10 | 2 | 3 | 10 | 9 | 8 | 9 |
| 39 | No | Travel_Frequently | 672 | Research & Development | 7 | 2 | Medical | 1 | 444 | 3 | Male | 54 | 2 | 5 | Manager | 4 | Married | 19272 | 21141 | 1 | Y | No | 15 | 3 | 1 | 80 | 1 | 21 | 2 | 3 | 21 | 9 | 13 | 3 |
| 39 | Yes | Travel_Rarely | 1162 | Sales | 3 | 2 | Medical | 1 | 445 | 4 | Female | 41 | 3 | 2 | Sales Executive | 3 | Married | 5238 | 17778 | 4 | Y | Yes | 18 | 3 | 1 | 80 | 0 | 12 | 3 | 2 | 1 | 0 | 0 | 0 |
| 33 | No | Travel_Frequently | 508 | Sales | 10 | 3 | Marketing | 1 | 446 | 2 | Male | 46 | 2 | 2 | Sales Executive | 4 | Single | 4682 | 4317 | 3 | Y | No | 14 | 3 | 3 | 80 | 0 | 9 | 6 | 2 | 7 | 7 | 0 | 1 |
| 47 | No | Travel_Rarely | 1482 | Research & Development | 5 | 5 | Life Sciences | 1 | 447 | 4 | Male | 42 | 3 | 5 | Research Director | 3 | Married | 18300 | 16375 | 4 | Y | No | 11 | 3 | 2 | 80 | 1 | 21 | 2 | 3 | 3 | 2 | 1 | 1 |
| 43 | No | Travel_Frequently | 559 | Research & Development | 10 | 4 | Life Sciences | 1 | 448 | 3 | Female | 82 | 2 | 2 | Laboratory Technician | 3 | Divorced | 5257 | 6227 | 1 | Y | No | 11 | 3 | 2 | 80 | 1 | 9 | 3 | 4 | 9 | 7 | 0 | 0 |
| NA | No | Non-Travel | 210 | Sales | 1 | 1 | Marketing | 1 | 449 | 3 | Male | 73 | 3 | 2 | Sales Executive | 2 | Married | 6349 | 22107 | 0 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 6 | 0 | 3 | 5 | 4 | 1 | 4 |
| 54 | No | Travel_Frequently | 928 | Research & Development | 20 | 4 | Life Sciences | 1 | 450 | 4 | Female | 31 | 3 | 2 | Research Scientist | 3 | Single | 4869 | 16885 | 3 | Y | No | 12 | 3 | 4 | 80 | 0 | 20 | 4 | 2 | 4 | 3 | 0 | 3 |
| 43 | No | Travel_Rarely | 1001 | Research & Development | 7 | 3 | Life Sciences | 1 | 451 | 3 | Female | 43 | 3 | 3 | Healthcare Representative | 1 | Married | 9985 | 9262 | 8 | Y | No | 16 | 3 | 1 | 80 | 1 | 10 | 1 | 2 | 1 | 0 | 0 | 0 |
| 45 | No | Travel_Rarely | 549 | Research & Development | 8 | 4 | Other | 1 | 452 | 4 | Male | 75 | 3 | 2 | Research Scientist | 4 | Married | 3697 | 9278 | 9 | Y | No | 14 | 3 | 1 | 80 | 2 | 12 | 3 | 3 | 10 | 9 | 9 | 8 |
| 40 | No | Travel_Rarely | 1124 | Sales | 1 | 2 | Medical | 1 | 453 | 2 | Male | 57 | 1 | 2 | Sales Executive | 4 | Married | 7457 | 13273 | 2 | Y | Yes | 22 | 4 | 3 | 80 | 3 | 6 | 2 | 2 | 4 | 3 | 0 | 2 |
| 29 | Yes | Travel_Rarely | 318 | Research & Development | 8 | 4 | Other | 1 | 454 | 2 | Male | 77 | 1 | 1 | Laboratory Technician | 1 | Married | NA | 4759 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 7 | 4 | 2 | 7 | 7 | 0 | 7 |
| 29 | No | Travel_Rarely | 738 | Research & Development | 9 | 5 | Other | 1 | 455 | 2 | Male | 30 | 2 | 1 | Laboratory Technician | 4 | Single | 3983 | 7621 | 0 | Y | No | 17 | 3 | 3 | 80 | 0 | 4 | 2 | 3 | 3 | 2 | 2 | 2 |
| NA | No | Travel_Rarely | 570 | Sales | 5 | 3 | Marketing | 1 | 456 | 4 | Female | 30 | 2 | 2 | Sales Executive | 3 | Divorced | 6118 | 5431 | 1 | Y | No | 13 | 3 | 3 | 80 | 3 | 10 | 2 | 3 | 10 | 9 | 1 | 2 |
| 27 | No | Travel_Rarely | 1130 | Sales | 8 | 4 | Marketing | 1 | 458 | 2 | Female | 56 | 3 | 2 | Sales Executive | 2 | Married | 6214 | 3415 | 1 | Y | No | 18 | 3 | 1 | 80 | 1 | 8 | 3 | 3 | 8 | 7 | 0 | 7 |
| 37 | No | Travel_Rarely | 1192 | Research & Development | 5 | 2 | Medical | 1 | 460 | 4 | Male | 61 | 3 | 2 | Manufacturing Director | 4 | Divorced | 6347 | 23177 | 7 | Y | No | 16 | 3 | 3 | 80 | 2 | 8 | 2 | 2 | 6 | 2 | 0 | 4 |
| 38 | No | Travel_Rarely | 343 | Research & Development | 15 | 2 | Life Sciences | 1 | 461 | 3 | Male | 92 | 2 | 3 | Research Director | 4 | Divorced | 11510 | 15682 | 0 | Y | Yes | 14 | 3 | 2 | 80 | 1 | 12 | 3 | 3 | 11 | 10 | 2 | 9 |
| 31 | No | Travel_Rarely | 1232 | Research & Development | 7 | 4 | Medical | 1 | 462 | 3 | Female | 39 | 3 | 3 | Manufacturing Director | 4 | Single | 7143 | 25713 | 1 | Y | Yes | 14 | 3 | 3 | 80 | 0 | 11 | 2 | 2 | 11 | 9 | 4 | 10 |
| 29 | No | Travel_Rarely | 144 | Sales | 10 | 1 | Marketing | 1 | 463 | 4 | Female | 39 | 2 | 2 | Sales Executive | 2 | Divorced | 8268 | 11866 | 1 | Y | Yes | 14 | 3 | 1 | 80 | 2 | 7 | 2 | 3 | 7 | 7 | 1 | 7 |
| 35 | No | Travel_Rarely | 1296 | Research & Development | 5 | 4 | Technical Degree | 1 | 464 | 3 | Male | 62 | 3 | 3 | Manufacturing Director | 2 | Single | 8095 | 18264 | 0 | Y | No | 13 | 3 | 4 | 80 | 0 | 17 | 5 | 3 | 16 | 6 | 0 | 13 |
| 23 | No | Travel_Rarely | 1309 | Research & Development | 26 | 1 | Life Sciences | 1 | 465 | 3 | Male | 83 | 3 | 1 | Research Scientist | 4 | Divorced | 2904 | 16092 | 1 | Y | No | 12 | 3 | 3 | 80 | 2 | 4 | 2 | 2 | 4 | 2 | 0 | 2 |
| 41 | NA | Travel_Rarely | 483 | Research & Development | 6 | 3 | Medical | 1 | 466 | 4 | Male | 95 | 2 | 2 | Manufacturing Director | 2 | Single | 6032 | 10110 | 6 | Y | Yes | 15 | 3 | 4 | 80 | 0 | 8 | 3 | 3 | 5 | 4 | 1 | 2 |
| 47 | NA | Travel_Frequently | 1309 | Sales | 4 | 1 | Medical | 1 | 467 | 2 | Male | 99 | 3 | 2 | Sales Representative | 3 | Single | 2976 | 25751 | 3 | Y | No | 19 | 3 | 1 | 80 | 0 | 5 | 3 | 3 | 0 | 0 | 0 | 0 |
| 42 | No | Travel_Rarely | 810 | Research & Development | 23 | 5 | Life Sciences | 1 | 468 | 1 | Female | 44 | 3 | 4 | Research Director | 4 | Single | 15992 | 15901 | 2 | Y | No | 14 | 3 | 2 | 80 | 0 | 16 | 2 | 3 | 1 | 0 | 0 | 0 |
| 29 | No | Non-Travel | 746 | Sales | 2 | 3 | Life Sciences | 1 | 469 | 4 | Male | 61 | 3 | 2 | Sales Executive | 3 | Married | 4649 | 16928 | 1 | Y | No | 14 | 3 | 1 | 80 | 1 | 4 | 3 | 2 | 4 | 3 | 0 | 2 |
| 42 | No | Travel_Rarely | 544 | Human Resources | 2 | 1 | Technical Degree | 1 | 470 | 3 | Male | 52 | 3 | 1 | Human Resources | 3 | Divorced | 2696 | 24017 | 0 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 4 | 5 | 3 | 3 | 2 | 1 | 0 |
| 32 | No | Travel_Rarely | 1062 | Research & Development | 2 | 3 | Medical | 1 | 471 | 3 | Female | 75 | 3 | 1 | Laboratory Technician | 2 | Married | 2370 | 3956 | 1 | Y | No | 13 | 3 | 3 | 80 | 1 | 8 | 4 | 3 | 8 | 0 | 0 | 7 |
| 48 | No | Travel_Rarely | 530 | Sales | 29 | 1 | Medical | 1 | 473 | 1 | Female | 91 | 3 | 3 | Manager | 3 | Married | 12504 | 23978 | 3 | Y | No | 21 | 4 | 2 | 80 | 1 | 15 | 3 | 1 | 0 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 1319 | Research & Development | 6 | 3 | Medical | 1 | 474 | 3 | Male | 51 | 4 | 2 | Research Scientist | 1 | Divorced | 5974 | 17001 | 4 | Y | Yes | 13 | 3 | 1 | 80 | 2 | 13 | 2 | 3 | 7 | 7 | 6 | 7 |
| 30 | No | Non-Travel | 641 | Sales | 25 | 2 | Technical Degree | 1 | 475 | 4 | Female | 85 | 3 | 2 | Sales Executive | 3 | Married | 4736 | 6069 | 7 | Y | Yes | 12 | 3 | 2 | 80 | 1 | 4 | 2 | 4 | 2 | 2 | 2 | 2 |
| 26 | NA | Travel_Rarely | 933 | Sales | 1 | 3 | Life Sciences | 1 | 476 | 3 | Male | 57 | 3 | 2 | Sales Executive | 3 | Married | 5296 | 20156 | 1 | Y | No | 17 | 3 | 2 | 80 | 1 | 8 | 3 | 3 | 8 | 7 | 7 | 7 |
| 42 | No | Travel_Rarely | 1332 | Research & Development | 2 | 4 | Other | 1 | 477 | 1 | Male | 98 | 2 | 2 | Healthcare Representative | 4 | Single | 6781 | 17078 | 3 | Y | No | 23 | 4 | 2 | 80 | 0 | 14 | 6 | 3 | 1 | 0 | 0 | 0 |
| NA | Yes | Travel_Frequently | 756 | Sales | 1 | 1 | Technical Degree | 1 | 478 | 1 | Female | 99 | 2 | 1 | Sales Representative | 2 | Single | 2174 | 9150 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 3 | 3 | 3 | 3 | 2 | 1 | 2 |
| 36 | NA | Non-Travel | 845 | Sales | 1 | 5 | Medical | 1 | 479 | 4 | Female | 45 | 3 | 2 | Sales Executive | 4 | Single | 6653 | 15276 | 4 | Y | No | 15 | 3 | 2 | 80 | 0 | 7 | 6 | 3 | 1 | 0 | 0 | 0 |
| 36 | No | Travel_Frequently | 541 | Sales | 3 | 4 | Medical | 1 | 481 | 1 | Male | 48 | 2 | 3 | Sales Executive | 4 | Married | 9699 | 7246 | 4 | Y | No | 11 | 3 | 1 | 80 | 1 | 16 | 2 | 3 | 13 | 9 | 1 | 12 |
| 57 | No | Travel_Rarely | 593 | Research & Development | 1 | 4 | Medical | 1 | 482 | 4 | Male | 88 | 3 | 2 | Healthcare Representative | 3 | Married | 6755 | 2967 | 2 | Y | No | 11 | 3 | 3 | 80 | 0 | 15 | 2 | 3 | 3 | 2 | 1 | 2 |
| 40 | No | Travel_Rarely | 1171 | Research & Development | 10 | 4 | Life Sciences | 1 | 483 | 4 | Female | 46 | 4 | 1 | Laboratory Technician | 3 | Married | 2213 | 22495 | 3 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 10 | 3 | 3 | 7 | 7 | 1 | 7 |
| 21 | No | Non-Travel | 895 | Sales | 9 | 2 | Medical | 1 | 484 | 1 | Male | 39 | 3 | 1 | Sales Representative | 4 | Single | 2610 | 2851 | 1 | Y | No | 24 | 4 | 3 | 80 | 0 | 3 | 3 | 2 | 3 | 2 | 2 | 2 |
| 33 | Yes | Travel_Rarely | 350 | Sales | 5 | 3 | Marketing | 1 | 485 | 4 | Female | 34 | 3 | 1 | Sales Representative | 3 | Single | 2851 | 9150 | 1 | Y | Yes | 13 | 3 | 2 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 921 | Research & Development | 10 | 3 | Medical | 1 | 486 | 3 | Female | 98 | 3 | 1 | Laboratory Technician | 1 | Married | 3452 | 17663 | 6 | Y | No | 20 | 4 | 2 | 80 | 1 | 17 | 3 | 3 | 5 | 4 | 0 | 3 |
| 46 | NA | Non-Travel | 1144 | Research & Development | 7 | 4 | Medical | 1 | 487 | 3 | Female | 30 | 3 | 2 | Manufacturing Director | 3 | Married | 5258 | 16044 | 2 | Y | No | 14 | 3 | 3 | 80 | 0 | 7 | 2 | 4 | 1 | 0 | 0 | 0 |
| 41 | Yes | Travel_Frequently | 143 | Sales | 4 | 3 | Marketing | 1 | 488 | 1 | Male | 56 | 3 | 2 | Sales Executive | 2 | Single | 9355 | 9558 | 1 | Y | No | 18 | 3 | 3 | 80 | 0 | 8 | 5 | 3 | 8 | 7 | 7 | 7 |
| 50 | No | Travel_Rarely | 1046 | Research & Development | 10 | 3 | Technical Degree | 1 | 491 | 4 | Male | 100 | 2 | 3 | Healthcare Representative | 4 | Single | 10496 | 2755 | 6 | Y | No | 15 | 3 | 4 | 80 | 0 | 20 | 2 | 3 | 4 | 3 | 1 | 3 |
| 40 | Yes | Travel_Rarely | 575 | Sales | 22 | 2 | Marketing | 1 | 492 | 3 | Male | 68 | 2 | 2 | Sales Executive | 3 | Married | 6380 | 6110 | 2 | Y | Yes | 12 | 3 | 1 | 80 | 2 | 8 | 6 | 3 | 6 | 4 | 1 | 0 |
| 31 | No | Travel_Rarely | 408 | Research & Development | 9 | 4 | Life Sciences | 1 | 493 | 3 | Male | 42 | 2 | 1 | Research Scientist | 2 | Single | 2657 | 7551 | 0 | Y | Yes | 16 | 3 | 4 | 80 | 0 | 3 | 5 | 3 | 2 | 2 | 2 | 2 |
| 21 | Yes | Travel_Rarely | 156 | Sales | 12 | 3 | Life Sciences | 1 | 494 | 3 | Female | 90 | 4 | 1 | Sales Representative | 2 | Single | 2716 | 25422 | 1 | Y | No | 15 | 3 | 4 | 80 | 0 | 1 | 0 | 3 | 1 | 0 | 0 | 0 |
| 29 | No | Travel_Rarely | 1283 | Research & Development | 23 | 3 | Life Sciences | 1 | 495 | 4 | Male | 54 | 3 | 1 | Research Scientist | 4 | Single | 2201 | 18168 | 9 | Y | No | 16 | 3 | 4 | 80 | 0 | 6 | 4 | 3 | 3 | 2 | 1 | 2 |
| 35 | No | Travel_Rarely | 755 | Research & Development | 9 | 4 | Life Sciences | 1 | 496 | 3 | Male | 97 | 2 | 2 | Healthcare Representative | 2 | Single | 6540 | 19394 | 9 | Y | No | 19 | 3 | 3 | 80 | 0 | 10 | 5 | 3 | 1 | 1 | 0 | 0 |
| 27 | No | Travel_Rarely | 1469 | Research & Development | 1 | 2 | Medical | 1 | 497 | 4 | Male | 82 | 3 | 1 | Laboratory Technician | 2 | Divorced | 3816 | 17881 | 1 | Y | No | 11 | 3 | 2 | 80 | 1 | 5 | 2 | 3 | 5 | 2 | 0 | 4 |
| 28 | NA | Travel_Rarely | 304 | Sales | 9 | 4 | Life Sciences | 1 | 498 | 2 | Male | 92 | 3 | 2 | Sales Executive | 4 | Single | 5253 | 20750 | 1 | Y | No | 16 | 3 | 4 | 80 | 0 | 7 | 1 | 3 | 7 | 5 | 0 | 7 |
| 49 | No | Travel_Rarely | 1261 | Research & Development | 7 | 3 | Other | 1 | 499 | 2 | Male | 31 | 2 | 3 | Healthcare Representative | 3 | Single | 10965 | 12066 | 8 | Y | No | 24 | 4 | 3 | 80 | 0 | 26 | 2 | 3 | 5 | 2 | 0 | 0 |
| 51 | No | Travel_Rarely | 1178 | Sales | 14 | 2 | Life Sciences | 1 | 500 | 3 | Female | 87 | 3 | 2 | Sales Executive | 4 | Married | 4936 | 14862 | 4 | Y | No | 11 | 3 | 3 | 80 | 1 | 18 | 2 | 2 | 7 | 7 | 0 | 7 |
| 36 | No | Travel_Rarely | 329 | Research & Development | 2 | 3 | Life Sciences | 1 | 501 | 4 | Female | 96 | 3 | 1 | Research Scientist | 3 | Married | 2543 | 11868 | 4 | Y | No | 13 | 3 | 2 | 80 | 1 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| NA | Yes | Non-Travel | 1362 | Sales | 19 | 3 | Marketing | 1 | 502 | 1 | Male | 67 | 4 | 2 | Sales Executive | 4 | Single | 5304 | 4652 | 8 | Y | Yes | 13 | 3 | 2 | 80 | 0 | 9 | 3 | 2 | 5 | 2 | 0 | 4 |
| 55 | No | Travel_Rarely | 1311 | Research & Development | 2 | 3 | Life Sciences | 1 | 505 | 3 | Female | 97 | 3 | 4 | Manager | 4 | Single | 16659 | 23258 | 2 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 30 | 2 | 3 | 5 | 4 | 1 | 2 |
| 24 | No | Travel_Rarely | 1371 | Sales | 10 | 4 | Marketing | 1 | 507 | 4 | Female | 77 | 3 | 2 | Sales Executive | 3 | Divorced | 4260 | 5915 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 1 | 5 | 2 | 4 | 5 | 2 | 0 | 3 |
| 30 | No | Travel_Rarely | 202 | Sales | 2 | 1 | Technical Degree | 1 | 508 | 3 | Male | 72 | 3 | 1 | Sales Representative | 2 | Married | 2476 | 17434 | 1 | Y | No | 18 | 3 | 1 | 80 | 1 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 26 | Yes | Travel_Frequently | 575 | Research & Development | 3 | 1 | Technical Degree | 1 | 510 | 3 | Male | 73 | 3 | 1 | Research Scientist | 1 | Single | 3102 | 6582 | 0 | Y | No | 22 | 4 | 3 | 80 | 0 | 7 | 2 | 3 | 6 | 4 | 0 | 4 |
| 22 | No | Travel_Rarely | 253 | Research & Development | 11 | 3 | Medical | 1 | 511 | 1 | Female | 43 | 3 | 1 | Research Scientist | 2 | Married | 2244 | 24440 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 2 | 1 | 3 | 2 | 1 | 1 | 2 |
| 36 | No | Travel_Rarely | 164 | Sales | 2 | 2 | Medical | 1 | 513 | 2 | Male | 61 | 2 | 3 | Sales Executive | 3 | Married | 7596 | 3809 | 1 | Y | No | 13 | 3 | 2 | 80 | 2 | 10 | 2 | 3 | 10 | 9 | 9 | 0 |
| 30 | Yes | Travel_Frequently | 464 | Research & Development | 4 | 3 | Technical Degree | 1 | 514 | 3 | Male | 40 | 3 | 1 | Research Scientist | 4 | Single | 2285 | 3427 | 9 | Y | Yes | 23 | 4 | 3 | 80 | 0 | 3 | 4 | 3 | 1 | 0 | 0 | 0 |
| NA | NA | Travel_Rarely | 1107 | Research & Development | 14 | 3 | Life Sciences | 1 | 515 | 4 | Female | 95 | 3 | 1 | Laboratory Technician | 1 | Divorced | 3034 | 26914 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 18 | 2 | 2 | 18 | 7 | 12 | 17 |
| 40 | No | Travel_Rarely | 759 | Sales | 2 | 2 | Marketing | 1 | 516 | 4 | Female | 46 | 3 | 2 | Sales Executive | 2 | Divorced | 5715 | 22553 | 7 | Y | No | 12 | 3 | 3 | 80 | 2 | 8 | 5 | 3 | 5 | 4 | 1 | 3 |
| 42 | No | Travel_Rarely | 201 | Research & Development | 1 | 4 | Life Sciences | 1 | 517 | 2 | Female | 95 | 3 | 1 | Laboratory Technician | 1 | Divorced | 2576 | 20490 | 3 | Y | No | 16 | 3 | 2 | 80 | 1 | 8 | 5 | 3 | 5 | 2 | 1 | 2 |
| 37 | NA | Travel_Rarely | 1305 | Research & Development | 10 | 4 | Life Sciences | 1 | 518 | 3 | Male | 49 | 3 | 2 | Manufacturing Director | 2 | Single | NA | 21123 | 2 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 18 | 2 | 2 | 1 | 0 | 0 | 1 |
| 43 | NA | Travel_Rarely | 982 | Research & Development | 12 | 3 | Life Sciences | 1 | 520 | 1 | Male | 59 | 2 | 4 | Research Director | 2 | Divorced | 14336 | 4345 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 25 | 3 | 3 | 25 | 10 | 3 | 9 |
| 40 | No | Travel_Rarely | 555 | Research & Development | 2 | 3 | Medical | 1 | 521 | 2 | Female | 78 | 2 | 2 | Laboratory Technician | 3 | Married | 3448 | 13436 | 6 | Y | No | 22 | 4 | 2 | 80 | 1 | 20 | 3 | 3 | 1 | 0 | 0 | 0 |
| 54 | No | Travel_Rarely | 821 | Research & Development | 5 | 2 | Medical | 1 | 522 | 1 | Male | 86 | 3 | 5 | Research Director | 1 | Married | 19406 | 8509 | 4 | Y | No | 11 | 3 | 3 | 80 | 1 | 24 | 4 | 2 | 4 | 2 | 1 | 2 |
| 34 | No | Non-Travel | 1381 | Sales | 4 | 4 | Marketing | 1 | 523 | 3 | Female | 72 | 3 | 2 | Sales Executive | 3 | Married | 6538 | 12740 | 9 | Y | No | 15 | 3 | 1 | 80 | 1 | 6 | 3 | 3 | 3 | 2 | 1 | 2 |
| 31 | No | Travel_Rarely | 480 | Research & Development | 7 | 2 | Medical | 1 | 524 | 2 | Female | 31 | 3 | 2 | Manufacturing Director | 1 | Married | 4306 | 4156 | 1 | Y | No | 12 | 3 | 2 | 80 | 1 | 13 | 5 | 1 | 13 | 10 | 3 | 12 |
| 43 | No | Travel_Frequently | 313 | Research & Development | 21 | 3 | Medical | 1 | 525 | 4 | Male | 61 | 3 | 1 | Laboratory Technician | 4 | Married | 2258 | 15238 | 7 | Y | No | 20 | 4 | 1 | 80 | 1 | 8 | 1 | 3 | 3 | 2 | 1 | 2 |
| 43 | No | Travel_Rarely | 1473 | Research & Development | 8 | 4 | Other | 1 | 526 | 3 | Female | 74 | 3 | 2 | Healthcare Representative | 3 | Divorced | 4522 | 2227 | 4 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 8 | 3 | 3 | 5 | 2 | 0 | 2 |
| 25 | No | Travel_Rarely | 891 | Sales | 4 | 2 | Life Sciences | 1 | 527 | 2 | Female | 99 | 2 | 2 | Sales Executive | 4 | Single | 4487 | 12090 | 1 | Y | Yes | 11 | 3 | 2 | 80 | 0 | 5 | 3 | 3 | 5 | 4 | 1 | 3 |
| 37 | No | Non-Travel | 1063 | Research & Development | 25 | 5 | Medical | 1 | 529 | 2 | Female | 72 | 3 | 2 | Research Scientist | 3 | Married | 4449 | 23866 | 3 | Y | Yes | 15 | 3 | 1 | 80 | 2 | 15 | 2 | 3 | 13 | 11 | 10 | 7 |
| 31 | No | Travel_Rarely | 329 | Research & Development | 1 | 2 | Life Sciences | 1 | 530 | 4 | Male | 98 | 2 | 1 | Laboratory Technician | 1 | Married | 2218 | 16193 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 4 | 3 | 3 | 4 | 2 | 3 | 2 |
| 39 | No | Travel_Frequently | 1218 | Research & Development | 1 | 1 | Life Sciences | 1 | 531 | 2 | Male | 52 | 3 | 5 | Manager | 3 | Divorced | 19197 | 8213 | 1 | Y | Yes | 14 | 3 | 3 | 80 | 1 | 21 | 3 | 3 | 21 | 8 | 1 | 6 |
| 56 | No | Travel_Frequently | 906 | Sales | 6 | 3 | Life Sciences | 1 | 532 | 3 | Female | 86 | 4 | 4 | Sales Executive | 1 | Married | NA | 18256 | 9 | Y | No | 11 | 3 | 4 | 80 | 3 | 36 | 0 | 2 | 7 | 7 | 7 | 7 |
| 30 | No | Travel_Rarely | 1082 | Sales | 12 | 3 | Technical Degree | 1 | 533 | 2 | Female | 83 | 3 | 2 | Sales Executive | 3 | Single | 6577 | 19558 | 0 | Y | No | 11 | 3 | 2 | 80 | 0 | 6 | 6 | 3 | 5 | 4 | 4 | 4 |
| 41 | No | Travel_Rarely | 645 | Sales | 1 | 3 | Marketing | 1 | 534 | 2 | Male | 49 | 4 | 3 | Sales Executive | 1 | Married | 8392 | 19566 | 1 | Y | No | 16 | 3 | 3 | 80 | 1 | 10 | 2 | 3 | 10 | 7 | 0 | 7 |
| 28 | No | Travel_Rarely | 1300 | Research & Development | 17 | 2 | Medical | 1 | 536 | 3 | Male | 79 | 3 | 2 | Laboratory Technician | 1 | Divorced | 4558 | 13535 | 1 | Y | No | 12 | 3 | 4 | 80 | 1 | 10 | 2 | 3 | 10 | 0 | 1 | 8 |
| 25 | Yes | Travel_Rarely | 688 | Research & Development | 3 | 3 | Medical | 1 | 538 | 1 | Male | 91 | 3 | 1 | Laboratory Technician | 1 | Married | 4031 | 9396 | 5 | Y | No | 13 | 3 | 3 | 80 | 1 | 6 | 5 | 3 | 2 | 2 | 0 | 2 |
| 52 | No | Travel_Rarely | 319 | Research & Development | 3 | 3 | Medical | 1 | 543 | 4 | Male | 39 | 2 | 3 | Manufacturing Director | 3 | Married | 7969 | 19609 | 2 | Y | Yes | 14 | 3 | 3 | 80 | 0 | 28 | 4 | 3 | 5 | 4 | 0 | 4 |
| 45 | No | Travel_Rarely | 192 | Research & Development | 10 | 2 | Life Sciences | 1 | 544 | 1 | Male | 69 | 3 | 1 | Research Scientist | 4 | Married | 2654 | 9655 | 3 | Y | No | 21 | 4 | 4 | 80 | 2 | 8 | 3 | 2 | 2 | 2 | 0 | 2 |
| 52 | No | Travel_Rarely | 1490 | Research & Development | 4 | 2 | Life Sciences | 1 | 546 | 4 | Female | 30 | 3 | 4 | Manager | 4 | Married | 16555 | 10310 | 2 | Y | No | 13 | 3 | 4 | 80 | 0 | 31 | 2 | 1 | 5 | 2 | 1 | 4 |
| 42 | No | Travel_Frequently | 532 | Research & Development | 29 | 2 | Life Sciences | 1 | 547 | 1 | Female | 92 | 3 | 2 | Research Scientist | 3 | Divorced | 4556 | 12932 | 2 | Y | No | 11 | 3 | 2 | 80 | 1 | 19 | 3 | 3 | 5 | 4 | 0 | 2 |
| 30 | No | Travel_Rarely | 317 | Research & Development | 2 | 3 | Life Sciences | 1 | 548 | 3 | Female | 43 | 1 | 2 | Manufacturing Director | 4 | Single | 6091 | 24793 | 2 | Y | No | 20 | 4 | 3 | 80 | 0 | 11 | 2 | 3 | 5 | 4 | 0 | 2 |
| 60 | No | Travel_Rarely | 422 | Research & Development | 7 | 3 | Life Sciences | 1 | 549 | 1 | Female | 41 | 3 | 5 | Manager | 1 | Married | 19566 | 3854 | 5 | Y | No | 11 | 3 | 4 | 80 | 0 | 33 | 5 | 1 | 29 | 8 | 11 | 10 |
| 46 | No | Travel_Rarely | 1485 | Research & Development | 18 | 3 | Medical | 1 | 550 | 3 | Female | 87 | 3 | 2 | Manufacturing Director | 3 | Divorced | 4810 | 26314 | 2 | Y | No | 14 | 3 | 3 | 80 | 1 | 19 | 5 | 2 | 10 | 7 | 0 | 8 |
| 42 | No | Travel_Frequently | 1368 | Research & Development | 28 | 4 | Technical Degree | 1 | 551 | 4 | Female | 88 | 2 | 2 | Healthcare Representative | 4 | Married | 4523 | 4386 | 0 | Y | No | 11 | 3 | 4 | 80 | 3 | 7 | 4 | 4 | 6 | 5 | 0 | 4 |
| 24 | Yes | Travel_Rarely | 1448 | Sales | 1 | 1 | Technical Degree | 1 | 554 | 1 | Female | 62 | 3 | 1 | Sales Representative | 2 | Single | 3202 | 21972 | 1 | Y | Yes | 16 | 3 | 2 | 80 | 0 | 6 | 4 | 3 | 5 | 3 | 1 | 4 |
| 34 | Yes | Travel_Frequently | 296 | Sales | 6 | 2 | Marketing | 1 | 555 | 4 | Female | 33 | 1 | 1 | Sales Representative | 3 | Divorced | 2351 | 12253 | 0 | Y | No | 16 | 3 | 4 | 80 | 1 | 3 | 3 | 2 | 2 | 2 | 1 | 0 |
| NA | No | Travel_Frequently | 1490 | Research & Development | 2 | 2 | Life Sciences | 1 | 556 | 4 | Male | 42 | 3 | 1 | Laboratory Technician | 4 | Married | NA | 12106 | 1 | Y | Yes | 23 | 4 | 3 | 80 | 1 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 40 | NA | Travel_Rarely | 1398 | Sales | 2 | 4 | Life Sciences | 1 | 558 | 3 | Female | 79 | 3 | 5 | Manager | 3 | Married | 18041 | 13022 | 0 | Y | No | 14 | 3 | 4 | 80 | 0 | 21 | 2 | 3 | 20 | 15 | 1 | 12 |
| 26 | No | Travel_Rarely | 1349 | Research & Development | 23 | 3 | Life Sciences | 1 | 560 | 1 | Female | 90 | 3 | 1 | Research Scientist | 4 | Divorced | 2886 | 3032 | 1 | Y | No | 22 | 4 | 2 | 80 | 2 | 3 | 3 | 1 | 3 | 2 | 0 | 2 |
| 30 | No | Non-Travel | 1400 | Research & Development | 3 | 3 | Life Sciences | 1 | 562 | 3 | Male | 53 | 3 | 1 | Laboratory Technician | 4 | Married | 2097 | 16734 | 4 | Y | No | 15 | 3 | 3 | 80 | 1 | 9 | 3 | 1 | 5 | 3 | 1 | 4 |
| 29 | NA | Travel_Rarely | 986 | Research & Development | 3 | 4 | Medical | 1 | 564 | 2 | Male | 93 | 2 | 3 | Research Director | 3 | Married | 11935 | 21526 | 1 | Y | No | 18 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 10 | 2 | 0 | 7 |
| 29 | Yes | Travel_Rarely | 408 | Research & Development | 25 | 5 | Technical Degree | 1 | 565 | 3 | Female | 71 | 2 | 1 | Research Scientist | 2 | Married | 2546 | 18300 | 5 | Y | No | 16 | 3 | 2 | 80 | 0 | 6 | 2 | 4 | 2 | 2 | 1 | 1 |
| NA | NA | Travel_Rarely | 489 | Human Resources | 2 | 2 | Technical Degree | 1 | 566 | 1 | Male | 52 | 2 | 1 | Human Resources | 4 | Single | 2564 | 18437 | 1 | Y | No | 12 | 3 | 3 | 80 | 0 | 1 | 3 | 4 | 1 | 0 | 0 | 0 |
| 30 | No | Non-Travel | 1398 | Sales | 22 | 4 | Other | 1 | 567 | 3 | Female | 69 | 3 | 3 | Sales Executive | 1 | Married | 8412 | 2890 | 0 | Y | No | 11 | 3 | 3 | 80 | 0 | 10 | 3 | 3 | 9 | 8 | 7 | 8 |
| 57 | No | Travel_Rarely | 210 | Sales | 29 | 3 | Marketing | 1 | 568 | 1 | Male | 56 | 2 | 4 | Manager | 4 | Divorced | 14118 | 22102 | 3 | Y | No | 12 | 3 | 3 | 80 | 1 | 32 | 3 | 2 | 1 | 0 | 0 | 0 |
| 50 | No | Travel_Rarely | 1099 | Research & Development | 29 | 4 | Life Sciences | 1 | 569 | 2 | Male | 88 | 2 | 4 | Manager | 3 | Married | 17046 | 9314 | 0 | Y | No | 15 | 3 | 2 | 80 | 1 | 28 | 2 | 3 | 27 | 10 | 15 | 7 |
| 30 | No | Non-Travel | 1116 | Research & Development | 2 | 3 | Medical | 1 | 571 | 3 | Female | 49 | 3 | 1 | Laboratory Technician | 4 | Single | 2564 | 7181 | 0 | Y | No | 14 | 3 | 3 | 80 | 0 | 12 | 2 | 2 | 11 | 7 | 6 | 7 |
| 60 | No | Travel_Frequently | 1499 | Sales | 28 | 3 | Marketing | 1 | 573 | 3 | Female | 80 | 2 | 3 | Sales Executive | 1 | Married | 10266 | 2845 | 4 | Y | No | 19 | 3 | 4 | 80 | 0 | 22 | 5 | 4 | 18 | 13 | 13 | 11 |
| 47 | No | Travel_Rarely | 983 | Research & Development | 2 | 2 | Medical | 1 | 574 | 1 | Female | 65 | 3 | 2 | Manufacturing Director | 4 | Divorced | 5070 | 7389 | 5 | Y | No | 13 | 3 | 3 | 80 | 3 | 20 | 2 | 3 | 5 | 0 | 0 | 4 |
| 46 | No | Travel_Rarely | 1009 | Research & Development | 2 | 3 | Life Sciences | 1 | 575 | 1 | Male | 51 | 3 | 4 | Research Director | 3 | Married | 17861 | 2288 | 6 | Y | No | 13 | 3 | 3 | 80 | 0 | 26 | 2 | 1 | 3 | 2 | 0 | 1 |
| 35 | No | Travel_Rarely | 144 | Research & Development | 22 | 3 | Life Sciences | 1 | 577 | 4 | Male | 46 | 1 | 1 | Laboratory Technician | 3 | Single | 4230 | 19225 | 0 | Y | No | 15 | 3 | 3 | 80 | 0 | 6 | 2 | 3 | 5 | 4 | 4 | 3 |
| 54 | No | Travel_Rarely | 548 | Research & Development | 8 | 4 | Life Sciences | 1 | 578 | 3 | Female | 42 | 3 | 2 | Laboratory Technician | 3 | Single | 3780 | 23428 | 7 | Y | No | 11 | 3 | 3 | 80 | 0 | 19 | 3 | 3 | 1 | 0 | 0 | 0 |
| 34 | No | Travel_Rarely | 1303 | Research & Development | 2 | 4 | Life Sciences | 1 | 579 | 4 | Male | 62 | 2 | 1 | Research Scientist | 3 | Divorced | 2768 | 8416 | 3 | Y | No | 12 | 3 | 3 | 80 | 1 | 14 | 3 | 3 | 7 | 3 | 5 | 7 |
| 46 | No | Travel_Rarely | 1125 | Sales | 10 | 3 | Marketing | 1 | 580 | 3 | Female | 94 | 2 | 3 | Sales Executive | 4 | Married | 9071 | 11563 | 2 | Y | Yes | 19 | 3 | 3 | 80 | 1 | 15 | 3 | 3 | 3 | 2 | 1 | 2 |
| 31 | No | Travel_Rarely | 1274 | Research & Development | 9 | 1 | Life Sciences | 1 | 581 | 3 | Male | 33 | 3 | 3 | Manufacturing Director | 2 | Divorced | 10648 | 14394 | 1 | Y | No | 25 | 4 | 4 | 80 | 1 | 13 | 6 | 4 | 13 | 8 | 0 | 8 |
| 33 | Yes | Travel_Rarely | 1277 | Research & Development | 15 | 1 | Medical | 1 | 582 | 2 | Male | 56 | 3 | 3 | Manager | 3 | Married | 13610 | 24619 | 7 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 15 | 2 | 4 | 7 | 6 | 7 | 7 |
| 33 | Yes | Travel_Rarely | 587 | Research & Development | 10 | 1 | Medical | 1 | 584 | 1 | Male | 38 | 1 | 1 | Laboratory Technician | 4 | Divorced | 3408 | 6705 | 7 | Y | No | 13 | 3 | 1 | 80 | 3 | 8 | 2 | 3 | 4 | 3 | 1 | 3 |
| 30 | NA | Travel_Rarely | 413 | Sales | 7 | 1 | Marketing | 1 | 585 | 4 | Male | 57 | 3 | 1 | Sales Representative | 2 | Single | 2983 | 18398 | 0 | Y | No | 14 | 3 | 1 | 80 | 0 | 4 | 3 | 3 | 3 | 2 | 1 | 2 |
| 35 | No | Travel_Rarely | 1276 | Research & Development | 16 | 3 | Life Sciences | 1 | 586 | 4 | Male | 72 | 3 | 3 | Healthcare Representative | 3 | Married | 7632 | 14295 | 4 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 8 | 7 | 0 | 0 |
| 31 | Yes | Travel_Frequently | 534 | Research & Development | 20 | 3 | Life Sciences | 1 | 587 | 1 | Male | 66 | 3 | 3 | Healthcare Representative | 3 | Married | 9824 | 22908 | 3 | Y | No | 12 | 3 | 1 | 80 | 0 | 12 | 2 | 3 | 1 | 0 | 0 | 0 |
| NA | Yes | Travel_Frequently | 988 | Human Resources | 23 | 3 | Human Resources | 1 | 590 | 2 | Female | 43 | 3 | 3 | Human Resources | 1 | Divorced | 9950 | 11533 | 9 | Y | Yes | 15 | 3 | 3 | 80 | 3 | 11 | 2 | 3 | 3 | 2 | 0 | 2 |
| 42 | No | Travel_Frequently | 1474 | Research & Development | 5 | 2 | Other | 1 | 591 | 2 | Male | 97 | 3 | 1 | Laboratory Technician | 3 | Married | 2093 | 9260 | 4 | Y | No | 17 | 3 | 4 | 80 | 1 | 8 | 4 | 3 | 2 | 2 | 2 | 0 |
| 36 | No | Non-Travel | 635 | Sales | 10 | 4 | Medical | 1 | 592 | 2 | Male | 32 | 3 | 3 | Sales Executive | 4 | Single | 9980 | 15318 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 10 | 3 | 2 | 10 | 3 | 9 | 7 |
| 22 | Yes | Travel_Frequently | 1368 | Research & Development | 4 | 1 | Technical Degree | 1 | 593 | 3 | Male | 99 | 2 | 1 | Laboratory Technician | 3 | Single | 3894 | 9129 | 5 | Y | No | 16 | 3 | 3 | 80 | 0 | 4 | 3 | 3 | 2 | 2 | 1 | 2 |
| 48 | No | Travel_Rarely | 163 | Sales | 2 | 5 | Marketing | 1 | 595 | 2 | Female | 37 | 3 | 2 | Sales Executive | 4 | Married | 4051 | 19658 | 2 | Y | No | 14 | 3 | 1 | 80 | 1 | 14 | 2 | 3 | 9 | 7 | 6 | 7 |
| 55 | No | Travel_Rarely | 1117 | Sales | 18 | 5 | Life Sciences | 1 | 597 | 1 | Female | 83 | 3 | 4 | Manager | 2 | Single | 16835 | 9873 | 3 | Y | No | 23 | 4 | 4 | 80 | 0 | 37 | 2 | 3 | 10 | 9 | 7 | 7 |
| 41 | No | Non-Travel | 267 | Sales | 10 | 2 | Life Sciences | 1 | 599 | 4 | Male | 56 | 3 | 2 | Sales Executive | 4 | Single | 6230 | 13430 | 7 | Y | No | 14 | 3 | 4 | 80 | 0 | 16 | 3 | 3 | 14 | 3 | 1 | 10 |
| 35 | No | Travel_Rarely | 619 | Sales | 1 | 3 | Marketing | 1 | 600 | 2 | Male | 85 | 3 | 2 | Sales Executive | 3 | Married | 4717 | 18659 | 9 | Y | No | 11 | 3 | 3 | 80 | 0 | 15 | 2 | 3 | 11 | 9 | 6 | 9 |
| 40 | No | Travel_Rarely | 302 | Research & Development | 6 | 3 | Life Sciences | 1 | 601 | 2 | Female | 75 | 3 | 4 | Manufacturing Director | 3 | Single | 13237 | 20364 | 7 | Y | No | 15 | 3 | 3 | 80 | 0 | 22 | 3 | 3 | 20 | 6 | 5 | 13 |
| 39 | No | Travel_Frequently | 443 | Research & Development | 8 | 1 | Life Sciences | 1 | 602 | 3 | Female | 48 | 3 | 1 | Laboratory Technician | 3 | Married | 3755 | 17872 | 1 | Y | No | 11 | 3 | 1 | 80 | 1 | 8 | 3 | 3 | 8 | 3 | 0 | 7 |
| 31 | NA | Travel_Rarely | 828 | Sales | 2 | 1 | Life Sciences | 1 | 604 | 2 | Male | 77 | 3 | 2 | Sales Executive | 4 | Single | 6582 | 8346 | 4 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 10 | 2 | 4 | 6 | 5 | 0 | 5 |
| 42 | No | Travel_Rarely | 319 | Research & Development | 24 | 3 | Medical | 1 | 605 | 4 | Male | 56 | 3 | 3 | Manufacturing Director | 1 | Married | 7406 | 6950 | 1 | Y | Yes | 21 | 4 | 4 | 80 | 1 | 10 | 5 | 2 | 10 | 9 | 5 | 8 |
| 45 | No | Travel_Rarely | 561 | Sales | 2 | 3 | Other | 1 | 606 | 4 | Male | 61 | 3 | 2 | Sales Executive | 2 | Married | 4805 | 16177 | 0 | Y | No | 19 | 3 | 2 | 80 | 1 | 9 | 3 | 4 | 8 | 7 | 3 | 7 |
| 26 | Yes | Travel_Frequently | 426 | Human Resources | 17 | 4 | Life Sciences | 1 | 608 | 2 | Female | 58 | 3 | 1 | Human Resources | 3 | Divorced | 2741 | 22808 | 0 | Y | Yes | 11 | 3 | 2 | 80 | 1 | 8 | 2 | 2 | 7 | 7 | 1 | 0 |
| 29 | NA | Travel_Rarely | 232 | Research & Development | 19 | 3 | Technical Degree | 1 | 611 | 4 | Male | 34 | 3 | 2 | Manufacturing Director | 4 | Divorced | 4262 | 22645 | 4 | Y | No | 12 | 3 | 2 | 80 | 2 | 8 | 2 | 4 | 3 | 2 | 1 | 2 |
| NA | No | Travel_Rarely | 922 | Research & Development | 1 | 5 | Medical | 1 | 612 | 1 | Female | 95 | 4 | 4 | Research Director | 3 | Divorced | 16184 | 22578 | 4 | Y | No | 19 | 3 | 3 | 80 | 1 | 10 | 2 | 3 | 6 | 1 | 0 | 5 |
| 31 | NA | Travel_Rarely | 688 | Sales | 7 | 3 | Life Sciences | 1 | 613 | 3 | Male | 44 | 2 | 3 | Manager | 4 | Divorced | 11557 | 25291 | 9 | Y | No | 21 | 4 | 3 | 80 | 1 | 10 | 3 | 2 | 5 | 4 | 0 | 1 |
| 18 | Yes | Travel_Frequently | 1306 | Sales | 5 | 3 | Marketing | 1 | 614 | 2 | Male | 69 | 3 | 1 | Sales Representative | 2 | Single | 1878 | 8059 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 |
| 40 | No | Non-Travel | 1094 | Sales | 28 | 3 | Other | 1 | 615 | 3 | Male | 58 | 1 | 3 | Sales Executive | 1 | Divorced | 10932 | 11373 | 3 | Y | No | 15 | 3 | 3 | 80 | 1 | 20 | 2 | 3 | 1 | 0 | 0 | 1 |
| 41 | No | Non-Travel | 509 | Research & Development | 2 | 4 | Other | 1 | 616 | 1 | Female | 62 | 2 | 2 | Healthcare Representative | 3 | Single | NA | 2112 | 2 | Y | Yes | 17 | 3 | 1 | 80 | 0 | 10 | 3 | 3 | 8 | 7 | 0 | 7 |
| 26 | No | Travel_Rarely | 775 | Sales | 29 | 2 | Medical | 1 | 618 | 1 | Male | 45 | 3 | 2 | Sales Executive | 3 | Divorced | 4306 | 4267 | 5 | Y | No | 12 | 3 | 1 | 80 | 2 | 8 | 5 | 3 | 0 | 0 | 0 | 0 |
| 35 | NA | Travel_Rarely | 195 | Sales | 1 | 3 | Medical | 1 | 620 | 1 | Female | 80 | 3 | 2 | Sales Executive | 3 | Single | 4859 | 6698 | 1 | Y | No | 16 | 3 | 4 | 80 | 0 | 5 | 3 | 3 | 5 | 4 | 0 | 3 |
| 34 | No | Travel_Rarely | 258 | Sales | 21 | 4 | Life Sciences | 1 | 621 | 4 | Male | 74 | 4 | 2 | Sales Executive | 4 | Single | 5337 | 19921 | 1 | Y | No | 12 | 3 | 4 | 80 | 0 | 10 | 3 | 3 | 10 | 7 | 5 | 7 |
| 26 | Yes | Travel_Rarely | 471 | Research & Development | 24 | 3 | Technical Degree | 1 | 622 | 3 | Male | 66 | 1 | 1 | Laboratory Technician | 4 | Single | 2340 | 23213 | 1 | Y | Yes | 18 | 3 | 2 | 80 | 0 | 1 | 3 | 1 | 1 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 799 | Research & Development | 1 | 3 | Technical Degree | 1 | 623 | 2 | Female | 59 | 3 | 3 | Manufacturing Director | 4 | Single | 7491 | 23848 | 4 | Y | No | 17 | 3 | 4 | 80 | 0 | 12 | 3 | 4 | 6 | 5 | 1 | 2 |
| 46 | No | Travel_Frequently | 1034 | Research & Development | 18 | 1 | Medical | 1 | 624 | 1 | Female | 86 | 3 | 3 | Healthcare Representative | 3 | Married | 10527 | 8984 | 5 | Y | No | 11 | 3 | 4 | 80 | 0 | 28 | 3 | 2 | 2 | 2 | 1 | 2 |
| 41 | No | Travel_Rarely | 1276 | Sales | 2 | 5 | Life Sciences | 1 | 625 | 2 | Female | 91 | 3 | 4 | Manager | 1 | Married | 16595 | 5626 | 7 | Y | No | 16 | 3 | 2 | 80 | 1 | 22 | 2 | 3 | 18 | 16 | 11 | 8 |
| 37 | No | Non-Travel | 142 | Sales | 9 | 4 | Medical | 1 | 626 | 1 | Male | 69 | 3 | 3 | Sales Executive | 2 | Divorced | 8834 | 24666 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 9 | 6 | 3 | 9 | 5 | 7 | 7 |
| 52 | NA | Travel_Rarely | 956 | Research & Development | 6 | 2 | Technical Degree | 1 | 630 | 4 | Male | 78 | 3 | 2 | Research Scientist | 1 | Divorced | 5577 | 22087 | 3 | Y | Yes | 12 | 3 | 2 | 80 | 2 | 18 | 3 | 3 | 10 | 9 | 6 | 9 |
| 32 | Yes | Non-Travel | 1474 | Sales | 11 | 4 | Other | 1 | 631 | 4 | Male | 60 | 4 | 2 | Sales Executive | 3 | Married | 4707 | 23914 | 8 | Y | No | 12 | 3 | 4 | 80 | 0 | 6 | 2 | 3 | 4 | 2 | 1 | 2 |
| 24 | No | Travel_Frequently | 535 | Sales | 24 | 3 | Medical | 1 | 632 | 4 | Male | 38 | 3 | 1 | Sales Representative | 4 | Married | NA | 5530 | 0 | Y | No | 13 | 3 | 3 | 80 | 2 | 3 | 3 | 3 | 2 | 2 | 2 | 1 |
| 38 | No | Travel_Rarely | 1495 | Research & Development | 10 | 3 | Medical | 1 | 634 | 3 | Female | 76 | 3 | 2 | Healthcare Representative | 3 | Married | 9824 | 22174 | 3 | Y | No | 19 | 3 | 3 | 80 | 1 | 18 | 4 | 3 | 1 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 446 | Research & Development | 1 | 4 | Life Sciences | 1 | 635 | 2 | Female | 65 | 3 | 2 | Manufacturing Director | 2 | Married | 6447 | 15701 | 6 | Y | No | 12 | 3 | 2 | 80 | 1 | 8 | 2 | 2 | 6 | 5 | 4 | 3 |
| NA | No | Travel_Rarely | 1245 | Research & Development | 18 | 4 | Life Sciences | 1 | 638 | 4 | Male | 58 | 2 | 5 | Research Director | 3 | Divorced | 19502 | 2125 | 1 | Y | Yes | 17 | 3 | 3 | 80 | 1 | 31 | 5 | 3 | 31 | 9 | 0 | 9 |
| 24 | No | Travel_Rarely | 691 | Research & Development | 23 | 3 | Medical | 1 | 639 | 2 | Male | 89 | 4 | 1 | Research Scientist | 4 | Married | 2725 | 21630 | 1 | Y | Yes | 11 | 3 | 2 | 80 | 2 | 6 | 3 | 3 | 6 | 5 | 1 | 4 |
| 26 | No | Travel_Rarely | 703 | Sales | 28 | 2 | Marketing | 1 | 641 | 1 | Male | 66 | 3 | 2 | Sales Executive | 2 | Married | 6272 | 7428 | 1 | Y | No | 20 | 4 | 4 | 80 | 2 | 6 | 5 | 4 | 5 | 3 | 1 | 4 |
| NA | No | Travel_Rarely | 823 | Research & Development | 17 | 2 | Other | 1 | 643 | 4 | Male | 94 | 2 | 1 | Laboratory Technician | 2 | Married | 2127 | 9100 | 1 | Y | No | 21 | 4 | 4 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 50 | No | Travel_Frequently | 1246 | Human Resources | 3 | 3 | Medical | 1 | 644 | 1 | Male | 99 | 3 | 5 | Manager | 2 | Married | 18200 | 7999 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 32 | 2 | 3 | 32 | 5 | 10 | 7 |
| 25 | No | Travel_Rarely | 622 | Sales | 13 | 1 | Medical | 1 | 645 | 2 | Male | 40 | 3 | 1 | Sales Representative | 3 | Married | 2096 | 26376 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 7 | 1 | 3 | 7 | 4 | 0 | 6 |
| 24 | Yes | Travel_Frequently | 1287 | Research & Development | 7 | 3 | Life Sciences | 1 | 647 | 1 | Female | 55 | 3 | 1 | Laboratory Technician | 3 | Married | 2886 | 14168 | 1 | Y | Yes | 16 | 3 | 4 | 80 | 1 | 6 | 4 | 3 | 6 | 3 | 1 | 2 |
| 30 | Yes | Travel_Frequently | 448 | Sales | 12 | 4 | Life Sciences | 1 | 648 | 2 | Male | 74 | 2 | 1 | Sales Representative | 1 | Married | 2033 | 14470 | 1 | Y | No | 18 | 3 | 3 | 80 | 1 | 1 | 2 | 4 | 1 | 0 | 0 | 0 |
| 34 | No | Travel_Rarely | 254 | Research & Development | 1 | 2 | Life Sciences | 1 | 649 | 2 | Male | 83 | 2 | 1 | Research Scientist | 4 | Married | 3622 | 22794 | 1 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 6 | 3 | 3 | 6 | 5 | 1 | 3 |
| 31 | Yes | Travel_Rarely | 1365 | Sales | 13 | 4 | Medical | 1 | 650 | 2 | Male | 46 | 3 | 2 | Sales Executive | 1 | Divorced | 4233 | 11512 | 2 | Y | No | 17 | 3 | 3 | 80 | 0 | 9 | 2 | 1 | 3 | 1 | 1 | 2 |
| 35 | No | Travel_Rarely | 538 | Research & Development | 25 | 2 | Other | 1 | 652 | 1 | Male | 54 | 2 | 2 | Laboratory Technician | 4 | Single | 3681 | 14004 | 4 | Y | No | 14 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 3 | 2 | 0 | 2 |
| 31 | No | Travel_Rarely | 525 | Sales | 6 | 4 | Medical | 1 | 653 | 1 | Male | 66 | 4 | 2 | Sales Executive | 4 | Divorced | NA | 6219 | 4 | Y | No | 22 | 4 | 4 | 80 | 2 | 13 | 4 | 4 | 7 | 7 | 5 | 7 |
| 27 | No | Travel_Rarely | 798 | Research & Development | 6 | 4 | Medical | 1 | 655 | 1 | Female | 66 | 2 | 1 | Research Scientist | 3 | Divorced | 2187 | 5013 | 0 | Y | No | 12 | 3 | 3 | 80 | 2 | 6 | 5 | 2 | 5 | 3 | 0 | 3 |
| 37 | NA | Travel_Rarely | 558 | Sales | 2 | 3 | Marketing | 1 | 656 | 4 | Male | 75 | 3 | 2 | Sales Executive | 3 | Married | 9602 | 3010 | 4 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 17 | 3 | 2 | 3 | 0 | 1 | 0 |
| 20 | No | Travel_Rarely | 959 | Research & Development | 1 | 3 | Life Sciences | 1 | 657 | 4 | Female | 83 | 2 | 1 | Research Scientist | 2 | Single | NA | 11757 | 1 | Y | No | 13 | 3 | 4 | 80 | 0 | 1 | 0 | 4 | 1 | 0 | 0 | 0 |
| 42 | No | Travel_Rarely | 622 | Research & Development | 2 | 4 | Life Sciences | 1 | 659 | 3 | Female | 81 | 3 | 2 | Healthcare Representative | 4 | Married | 4089 | 5718 | 1 | Y | No | 13 | 3 | 2 | 80 | 2 | 10 | 4 | 3 | 10 | 2 | 2 | 2 |
| NA | No | Travel_Rarely | 782 | Research & Development | 6 | 4 | Other | 1 | 661 | 2 | Male | 50 | 2 | 4 | Research Director | 4 | Divorced | 16627 | 2671 | 4 | Y | Yes | 14 | 3 | 3 | 80 | 1 | 21 | 3 | 2 | 1 | 0 | 0 | 0 |
| 38 | No | Travel_Rarely | 362 | Research & Development | 1 | 1 | Life Sciences | 1 | 662 | 3 | Female | 43 | 3 | 1 | Research Scientist | 1 | Single | 2619 | 14561 | 3 | Y | No | 17 | 3 | 4 | 80 | 0 | 8 | 3 | 2 | 0 | 0 | 0 | 0 |
| 43 | No | Travel_Frequently | 1001 | Research & Development | 9 | 5 | Medical | 1 | 663 | 4 | Male | 72 | 3 | 2 | Laboratory Technician | 3 | Divorced | 5679 | 19627 | 3 | Y | Yes | 13 | 3 | 2 | 80 | 1 | 10 | 3 | 3 | 8 | 7 | 4 | 7 |
| 48 | No | Travel_Rarely | 1236 | Research & Development | 1 | 4 | Life Sciences | 1 | 664 | 4 | Female | 40 | 2 | 4 | Manager | 1 | Married | 15402 | 17997 | 7 | Y | No | 11 | 3 | 1 | 80 | 1 | 21 | 3 | 1 | 3 | 2 | 0 | 2 |
| 44 | No | Travel_Rarely | 1112 | Human Resources | 1 | 4 | Life Sciences | 1 | 665 | 1 | Female | 50 | 2 | 2 | Human Resources | 3 | Single | 5985 | 26894 | 4 | Y | No | 11 | 3 | 2 | 80 | 0 | 10 | 1 | 4 | 2 | 2 | 0 | 2 |
| 34 | No | Travel_Rarely | 204 | Sales | 14 | 3 | Technical Degree | 1 | 666 | 3 | Female | 31 | 3 | 1 | Sales Representative | 3 | Divorced | 2579 | 2912 | 1 | Y | Yes | 18 | 3 | 4 | 80 | 2 | 8 | 3 | 3 | 8 | 2 | 0 | 6 |
| 27 | Yes | Travel_Rarely | 1420 | Sales | 2 | 1 | Marketing | 1 | 667 | 3 | Male | 85 | 3 | 1 | Sales Representative | 1 | Divorced | 3041 | 16346 | 0 | Y | No | 11 | 3 | 2 | 80 | 1 | 5 | 3 | 3 | 4 | 3 | 0 | 2 |
| 21 | No | Travel_Rarely | 1343 | Sales | 22 | 1 | Technical Degree | 1 | 669 | 3 | Male | 49 | 3 | 1 | Sales Representative | 3 | Single | 3447 | 24444 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 3 | 2 | 3 | 3 | 2 | 1 | 2 |
| 44 | No | Travel_Rarely | 1315 | Research & Development | 3 | 4 | Other | 1 | 671 | 4 | Male | 35 | 3 | 5 | Manager | 4 | Married | 19513 | 9358 | 4 | Y | Yes | 12 | 3 | 1 | 80 | 1 | 26 | 2 | 4 | 2 | 2 | 0 | 1 |
| 22 | No | Travel_Rarely | 604 | Research & Development | 6 | 1 | Medical | 1 | 675 | 1 | Male | 69 | 3 | 1 | Research Scientist | 3 | Married | 2773 | 12145 | 0 | Y | No | 20 | 4 | 4 | 80 | 0 | 3 | 3 | 3 | 2 | 2 | 2 | 2 |
| 33 | No | Travel_Rarely | 1216 | Sales | 8 | 4 | Marketing | 1 | 677 | 3 | Male | 39 | 3 | 2 | Sales Executive | 3 | Divorced | 7104 | 20431 | 0 | Y | No | 12 | 3 | 4 | 80 | 0 | 6 | 3 | 3 | 5 | 0 | 1 | 2 |
| 32 | No | Travel_Rarely | 646 | Research & Development | 9 | 4 | Life Sciences | 1 | 679 | 1 | Female | 92 | 3 | 2 | Research Scientist | 4 | Married | NA | 18089 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 1 | 6 | 2 | 2 | 6 | 4 | 0 | 5 |
| 30 | No | Travel_Frequently | 160 | Research & Development | 3 | 3 | Medical | 1 | 680 | 3 | Female | 71 | 3 | 1 | Research Scientist | 3 | Divorced | 2083 | 22653 | 1 | Y | No | 20 | 4 | 3 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 53 | No | Travel_Rarely | 238 | Sales | 1 | 1 | Medical | 1 | 682 | 4 | Female | 34 | 3 | 2 | Sales Executive | 1 | Single | 8381 | 7507 | 7 | Y | No | 20 | 4 | 4 | 80 | 0 | 18 | 2 | 4 | 14 | 7 | 8 | 10 |
| 34 | No | Travel_Rarely | 1397 | Research & Development | 1 | 5 | Life Sciences | 1 | 683 | 2 | Male | 42 | 3 | 1 | Research Scientist | 4 | Married | 2691 | 7660 | 1 | Y | No | 12 | 3 | 4 | 80 | 1 | 10 | 4 | 2 | 10 | 9 | 8 | 8 |
| 45 | Yes | Travel_Frequently | 306 | Sales | 26 | 4 | Life Sciences | 1 | 684 | 1 | Female | 100 | 3 | 2 | Sales Executive | 1 | Married | 4286 | 5630 | 2 | Y | No | 14 | 3 | 4 | 80 | 2 | 5 | 4 | 3 | 1 | 1 | 0 | 0 |
| 26 | No | Travel_Rarely | 991 | Research & Development | 6 | 3 | Life Sciences | 1 | 686 | 3 | Female | 71 | 3 | 1 | Laboratory Technician | 4 | Married | 2659 | 17759 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 3 | 2 | 3 | 3 | 2 | 0 | 2 |
| 37 | No | Travel_Rarely | 482 | Research & Development | 3 | 3 | Other | 1 | 689 | 3 | Male | 36 | 3 | 3 | Manufacturing Director | 3 | Married | 9434 | 9606 | 1 | Y | No | 15 | 3 | 3 | 80 | 1 | 10 | 2 | 3 | 10 | 7 | 7 | 8 |
| 29 | No | Travel_Rarely | 1176 | Sales | 3 | 2 | Medical | 1 | 690 | 2 | Female | 62 | 3 | 2 | Sales Executive | 3 | Married | 5561 | 3487 | 1 | Y | No | 14 | 3 | 1 | 80 | 1 | 6 | 5 | 2 | 6 | 0 | 1 | 2 |
| 35 | No | Travel_Rarely | 1017 | Research & Development | 6 | 4 | Life Sciences | 1 | 691 | 2 | Male | 82 | 1 | 2 | Research Scientist | 4 | Single | 6646 | 19368 | 1 | Y | No | 13 | 3 | 2 | 80 | 0 | 17 | 3 | 3 | 17 | 11 | 11 | 8 |
| 33 | No | Travel_Frequently | 1296 | Research & Development | 6 | 3 | Life Sciences | 1 | 692 | 3 | Male | 30 | 3 | 2 | Healthcare Representative | 4 | Divorced | 7725 | 5335 | 3 | Y | No | 23 | 4 | 3 | 80 | 1 | 15 | 2 | 1 | 13 | 11 | 4 | 7 |
| 54 | No | Travel_Rarely | 397 | Human Resources | 19 | 4 | Medical | 1 | 698 | 3 | Male | 88 | 3 | 3 | Human Resources | 2 | Married | 10725 | 6729 | 2 | Y | No | 15 | 3 | 3 | 80 | 1 | 16 | 1 | 4 | 9 | 7 | 7 | 1 |
| 36 | No | Travel_Rarely | 913 | Research & Development | 9 | 2 | Medical | 1 | 699 | 2 | Male | 48 | 2 | 2 | Manufacturing Director | 2 | Divorced | 8847 | 13934 | 2 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 13 | 2 | 3 | 3 | 2 | 0 | 2 |
| 27 | No | Travel_Rarely | 1115 | Research & Development | 3 | 4 | Medical | 1 | 700 | 1 | Male | 54 | 2 | 1 | Research Scientist | 4 | Single | 2045 | 15174 | 0 | Y | No | 13 | 3 | 4 | 80 | 0 | 5 | 0 | 3 | 4 | 2 | 1 | 1 |
| 20 | Yes | Travel_Rarely | 1362 | Research & Development | 10 | 1 | Medical | 1 | 701 | 4 | Male | 32 | 3 | 1 | Research Scientist | 3 | Single | NA | 26999 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 1 | 5 | 3 | 1 | 0 | 1 | 1 |
| 33 | Yes | Travel_Frequently | 1076 | Research & Development | 3 | 3 | Life Sciences | 1 | 702 | 1 | Male | 70 | 3 | 1 | Research Scientist | 1 | Single | 3348 | 3164 | 1 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 10 | 3 | 3 | 10 | 8 | 9 | 7 |
| 35 | No | Non-Travel | 727 | Research & Development | 3 | 3 | Life Sciences | 1 | 704 | 3 | Male | 41 | 2 | 1 | Laboratory Technician | 3 | Married | 1281 | 16900 | 1 | Y | No | 18 | 3 | 3 | 80 | 2 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 23 | No | Travel_Rarely | 885 | Research & Development | 4 | 3 | Medical | 1 | 705 | 1 | Male | 58 | 4 | 1 | Research Scientist | 1 | Married | NA | 8544 | 2 | Y | No | 16 | 3 | 1 | 80 | 1 | 5 | 3 | 4 | 3 | 2 | 0 | 2 |
| 25 | No | Travel_Rarely | 810 | Sales | 8 | 3 | Life Sciences | 1 | 707 | 4 | Male | 57 | 4 | 2 | Sales Executive | 2 | Married | 4851 | 15678 | 0 | Y | No | 22 | 4 | 3 | 80 | 1 | 4 | 4 | 3 | 3 | 2 | 1 | 2 |
| 38 | No | Travel_Rarely | 243 | Sales | 7 | 4 | Marketing | 1 | 709 | 4 | Female | 46 | 2 | 2 | Sales Executive | 4 | Single | 4028 | 7791 | 0 | Y | No | 20 | 4 | 1 | 80 | 0 | 8 | 2 | 3 | 7 | 7 | 0 | 5 |
| 29 | No | Travel_Frequently | 806 | Research & Development | 1 | 4 | Life Sciences | 1 | 710 | 2 | Male | 76 | 1 | 1 | Research Scientist | 4 | Divorced | 2720 | 18959 | 1 | Y | No | 18 | 3 | 4 | 80 | 1 | 10 | 5 | 3 | 10 | 7 | 2 | 8 |
| 48 | No | Travel_Rarely | 817 | Sales | 2 | 1 | Marketing | 1 | 712 | 2 | Male | 56 | 4 | 2 | Sales Executive | 2 | Married | 8120 | 18597 | 3 | Y | No | 12 | 3 | 4 | 80 | 0 | 12 | 3 | 3 | 2 | 2 | 2 | 2 |
| 27 | NA | Travel_Frequently | 1410 | Sales | 3 | 1 | Medical | 1 | 714 | 4 | Female | 71 | 4 | 2 | Sales Executive | 4 | Divorced | NA | 16673 | 1 | Y | Yes | 20 | 4 | 2 | 80 | 2 | 6 | 3 | 3 | 6 | 5 | 0 | 4 |
| 37 | No | Travel_Rarely | 1225 | Research & Development | 10 | 2 | Life Sciences | 1 | 715 | 4 | Male | 80 | 4 | 1 | Research Scientist | 4 | Single | 4680 | 15232 | 3 | Y | No | 17 | 3 | 1 | 80 | 0 | 4 | 2 | 3 | 1 | 0 | 0 | 0 |
| 50 | No | Travel_Rarely | 1207 | Research & Development | 28 | 1 | Medical | 1 | 716 | 4 | Male | 74 | 4 | 1 | Laboratory Technician | 3 | Married | 3221 | 3297 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 3 | 20 | 3 | 3 | 20 | 8 | 3 | 8 |
| 34 | No | Travel_Rarely | 1442 | Research & Development | 9 | 3 | Medical | 1 | 717 | 4 | Female | 46 | 2 | 3 | Healthcare Representative | 2 | Single | 8621 | 17654 | 1 | Y | No | 14 | 3 | 2 | 80 | 0 | 9 | 3 | 4 | 8 | 7 | 7 | 7 |
| 24 | NA | Travel_Rarely | 693 | Sales | 3 | 2 | Life Sciences | 1 | 720 | 1 | Female | 65 | 3 | 2 | Sales Executive | 3 | Single | 4577 | 24785 | 9 | Y | No | 14 | 3 | 1 | 80 | 0 | 4 | 3 | 3 | 2 | 2 | 2 | 0 |
| NA | No | Travel_Rarely | 408 | Research & Development | 2 | 4 | Technical Degree | 1 | 721 | 4 | Female | 80 | 2 | 2 | Healthcare Representative | 3 | Single | 4553 | 20978 | 1 | Y | No | 11 | 3 | 1 | 80 | 0 | 20 | 4 | 3 | 20 | 7 | 11 | 10 |
| 32 | No | Travel_Rarely | 929 | Sales | 10 | 3 | Marketing | 1 | 722 | 4 | Male | 55 | 3 | 2 | Sales Executive | 4 | Single | 5396 | 21703 | 1 | Y | No | 12 | 3 | 4 | 80 | 0 | 10 | 2 | 2 | 10 | 7 | 0 | 8 |
| NA | Yes | Travel_Frequently | 562 | Sales | 8 | 2 | Technical Degree | 1 | 723 | 2 | Male | 50 | 3 | 2 | Sales Executive | 3 | Married | 6796 | 23452 | 3 | Y | Yes | 14 | 3 | 1 | 80 | 1 | 18 | 4 | 3 | 4 | 3 | 1 | 3 |
| NA | No | Travel_Rarely | 827 | Research & Development | 1 | 4 | Life Sciences | 1 | 724 | 2 | Female | 33 | 4 | 2 | Healthcare Representative | 4 | Single | 7625 | 19383 | 0 | Y | No | 13 | 3 | 3 | 80 | 0 | 10 | 4 | 2 | 9 | 7 | 1 | 8 |
| 27 | No | Travel_Rarely | 608 | Research & Development | 1 | 2 | Life Sciences | 1 | 725 | 3 | Female | 68 | 3 | 3 | Manufacturing Director | 1 | Married | 7412 | 6009 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 9 | 7 | 0 | 7 |
| 32 | No | Travel_Rarely | 1018 | Research & Development | 3 | 2 | Life Sciences | 1 | 727 | 3 | Female | 39 | 3 | 3 | Research Director | 4 | Single | 11159 | 19373 | 3 | Y | No | 15 | 3 | 4 | 80 | 0 | 10 | 6 | 3 | 7 | 7 | 7 | 7 |
| 47 | No | Travel_Rarely | 703 | Sales | 14 | 4 | Marketing | 1 | 728 | 4 | Male | 42 | 3 | 2 | Sales Executive | 1 | Single | 4960 | 11825 | 2 | Y | No | 12 | 3 | 4 | 80 | 0 | 20 | 2 | 3 | 7 | 7 | 1 | 7 |
| 40 | No | Travel_Frequently | 580 | Sales | 5 | 4 | Life Sciences | 1 | 729 | 4 | Male | 48 | 2 | 3 | Sales Executive | 1 | Married | 10475 | 23772 | 5 | Y | Yes | 21 | 4 | 3 | 80 | 1 | 20 | 2 | 3 | 18 | 13 | 1 | 12 |
| 53 | No | Travel_Rarely | 970 | Research & Development | 7 | 3 | Life Sciences | 1 | 730 | 3 | Male | 59 | 4 | 4 | Research Director | 3 | Married | 14814 | 13514 | 3 | Y | No | 19 | 3 | 3 | 80 | 0 | 32 | 3 | 3 | 5 | 1 | 1 | 3 |
| 41 | NA | Travel_Rarely | 427 | Human Resources | 10 | 4 | Human Resources | 1 | 731 | 2 | Male | 73 | 2 | 5 | Manager | 4 | Divorced | 19141 | 8861 | 3 | Y | No | 15 | 3 | 2 | 80 | 3 | 23 | 2 | 2 | 21 | 6 | 12 | 6 |
| 60 | No | Travel_Rarely | 1179 | Sales | 16 | 4 | Marketing | 1 | 732 | 1 | Male | 84 | 3 | 2 | Sales Executive | 1 | Single | 5405 | 11924 | 8 | Y | No | 14 | 3 | 4 | 80 | 0 | 10 | 1 | 3 | 2 | 2 | 2 | 2 |
| 27 | No | Travel_Frequently | 294 | Research & Development | 10 | 2 | Life Sciences | 1 | 733 | 4 | Male | 32 | 3 | 3 | Manufacturing Director | 1 | Divorced | 8793 | 4809 | 1 | Y | No | 21 | 4 | 3 | 80 | 2 | 9 | 4 | 2 | 9 | 7 | 1 | 7 |
| 41 | No | Travel_Rarely | 314 | Human Resources | 1 | 3 | Human Resources | 1 | 734 | 4 | Male | 59 | 2 | 5 | Manager | 3 | Married | 19189 | 19562 | 1 | Y | No | 12 | 3 | 2 | 80 | 1 | 22 | 3 | 3 | 22 | 7 | 2 | 10 |
| 50 | No | Travel_Rarely | 316 | Sales | 8 | 4 | Marketing | 1 | 738 | 4 | Male | 54 | 3 | 1 | Sales Representative | 2 | Married | 3875 | 9983 | 7 | Y | No | 15 | 3 | 4 | 80 | 1 | 4 | 2 | 3 | 2 | 2 | 2 | 2 |
| NA | Yes | Travel_Rarely | 654 | Research & Development | 1 | 2 | Life Sciences | 1 | 741 | 1 | Female | 67 | 1 | 1 | Research Scientist | 2 | Single | 2216 | 3872 | 7 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 10 | 4 | 3 | 7 | 7 | 3 | 7 |
| 36 | No | Non-Travel | 427 | Research & Development | 8 | 3 | Life Sciences | 1 | 742 | 1 | Female | 63 | 4 | 3 | Research Director | 1 | Married | 11713 | 20335 | 9 | Y | No | 14 | 3 | 1 | 80 | 1 | 10 | 2 | 3 | 8 | 7 | 0 | 5 |
| 38 | No | Travel_Rarely | 168 | Research & Development | 1 | 3 | Life Sciences | 1 | 743 | 3 | Female | 81 | 3 | 3 | Manufacturing Director | 3 | Single | 7861 | 15397 | 4 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 10 | 4 | 4 | 1 | 0 | 0 | 0 |
| 44 | No | Non-Travel | 381 | Research & Development | 24 | 3 | Medical | 1 | 744 | 1 | Male | 49 | 1 | 1 | Laboratory Technician | 3 | Single | NA | 2104 | 2 | Y | No | 14 | 3 | 3 | 80 | 0 | 9 | 5 | 3 | 5 | 2 | 1 | 4 |
| 47 | NA | Travel_Frequently | 217 | Sales | 3 | 3 | Medical | 1 | 746 | 4 | Female | 49 | 3 | 4 | Sales Executive | 3 | Divorced | 13770 | 10225 | 9 | Y | Yes | 12 | 3 | 4 | 80 | 2 | 28 | 2 | 2 | 22 | 2 | 11 | 13 |
| 30 | NA | Travel_Rarely | 501 | Sales | 27 | 5 | Marketing | 1 | 747 | 3 | Male | 99 | 3 | 2 | Sales Executive | 4 | Divorced | 5304 | 25275 | 7 | Y | No | 23 | 4 | 4 | 80 | 1 | 10 | 2 | 2 | 8 | 7 | 7 | 7 |
| 29 | No | Travel_Rarely | 1396 | Sales | 10 | 3 | Life Sciences | 1 | 749 | 3 | Male | 99 | 3 | 1 | Sales Representative | 3 | Single | 2642 | 2755 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 1 | 6 | 3 | 1 | 0 | 0 | 0 |
| 42 | Yes | Travel_Frequently | 933 | Research & Development | 19 | 3 | Medical | 1 | 752 | 3 | Male | 57 | 4 | 1 | Research Scientist | 3 | Divorced | 2759 | 20366 | 6 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 7 | 2 | 3 | 2 | 2 | 2 | 2 |
| 43 | No | Travel_Frequently | 775 | Sales | 15 | 3 | Life Sciences | 1 | 754 | 4 | Male | 47 | 2 | 2 | Sales Executive | 4 | Married | 6804 | 23683 | 3 | Y | No | 18 | 3 | 3 | 80 | 1 | 7 | 5 | 3 | 2 | 2 | 2 | 2 |
| 34 | No | Travel_Rarely | 970 | Research & Development | 8 | 2 | Medical | 1 | 757 | 2 | Female | 96 | 3 | 2 | Healthcare Representative | 3 | Single | 6142 | 7360 | 3 | Y | No | 11 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 5 | 1 | 4 | 3 |
| 23 | No | Travel_Rarely | 650 | Research & Development | 9 | 1 | Medical | 1 | 758 | 2 | Male | 37 | 3 | 1 | Laboratory Technician | 1 | Married | 2500 | 4344 | 1 | Y | No | 14 | 3 | 4 | 80 | 1 | 5 | 2 | 4 | 4 | 3 | 0 | 2 |
| 39 | No | Travel_Rarely | 141 | Human Resources | 3 | 3 | Human Resources | 1 | 760 | 3 | Female | 44 | 4 | 2 | Human Resources | 2 | Married | 6389 | 18767 | 9 | Y | No | 15 | 3 | 3 | 80 | 1 | 12 | 3 | 1 | 8 | 3 | 3 | 6 |
| 56 | No | Travel_Rarely | 832 | Research & Development | 9 | 3 | Medical | 1 | 762 | 3 | Male | 81 | 3 | 4 | Healthcare Representative | 4 | Married | NA | 20420 | 7 | Y | No | 11 | 3 | 3 | 80 | 0 | 30 | 1 | 2 | 10 | 7 | 1 | 1 |
| 40 | No | Travel_Rarely | 804 | Research & Development | 2 | 1 | Medical | 1 | 763 | 4 | Female | 86 | 2 | 1 | Research Scientist | 4 | Single | 2342 | 22929 | 0 | Y | Yes | 20 | 4 | 4 | 80 | 0 | 5 | 2 | 2 | 4 | 2 | 2 | 3 |
| 27 | No | Travel_Rarely | 975 | Research & Development | 7 | 3 | Medical | 1 | 764 | 4 | Female | 55 | 2 | 2 | Healthcare Representative | 1 | Single | 6811 | 23398 | 8 | Y | No | 19 | 3 | 1 | 80 | 0 | 9 | 2 | 1 | 7 | 6 | 0 | 7 |
| 29 | No | Travel_Rarely | 1090 | Sales | 10 | 3 | Marketing | 1 | 766 | 4 | Male | 83 | 3 | 1 | Sales Representative | 2 | Divorced | 2297 | 17967 | 1 | Y | No | 14 | 3 | 4 | 80 | 2 | 2 | 2 | 3 | 2 | 2 | 2 | 2 |
| 53 | NA | Travel_Rarely | 346 | Research & Development | 6 | 3 | Life Sciences | 1 | 769 | 4 | Male | 86 | 3 | 2 | Laboratory Technician | 4 | Single | 2450 | 10919 | 2 | Y | No | 17 | 3 | 4 | 80 | 0 | 19 | 4 | 3 | 2 | 2 | 2 | 2 |
| 35 | No | Non-Travel | 1225 | Research & Development | 2 | 4 | Life Sciences | 1 | 771 | 4 | Female | 61 | 3 | 2 | Healthcare Representative | 1 | Divorced | 5093 | 4761 | 2 | Y | No | 11 | 3 | 1 | 80 | 1 | 16 | 2 | 4 | 1 | 0 | 0 | 0 |
| 32 | No | Travel_Frequently | 430 | Research & Development | 24 | 4 | Life Sciences | 1 | 772 | 1 | Male | 80 | 3 | 2 | Laboratory Technician | 4 | Married | NA | 21146 | 1 | Y | No | 15 | 3 | 4 | 80 | 2 | 10 | 2 | 3 | 10 | 8 | 4 | 7 |
| 38 | No | Travel_Rarely | 268 | Research & Development | 2 | 5 | Medical | 1 | 773 | 4 | Male | 92 | 3 | 1 | Research Scientist | 3 | Married | 3057 | 20471 | 6 | Y | Yes | 13 | 3 | 2 | 80 | 1 | 6 | 0 | 1 | 1 | 0 | 0 | 1 |
| 34 | No | Travel_Rarely | 167 | Research & Development | 8 | 5 | Life Sciences | 1 | 775 | 2 | Female | 32 | 3 | 2 | Manufacturing Director | 1 | Divorced | NA | 4187 | 3 | Y | No | 14 | 3 | 3 | 80 | 1 | 7 | 3 | 3 | 0 | 0 | 0 | 0 |
| 52 | No | Travel_Rarely | 621 | Sales | 3 | 4 | Marketing | 1 | 776 | 3 | Male | 31 | 2 | 4 | Manager | 1 | Married | 16856 | 10084 | 1 | Y | No | 11 | 3 | 1 | 80 | 0 | 34 | 3 | 4 | 34 | 6 | 1 | 16 |
| 33 | NA | Travel_Rarely | 527 | Research & Development | 1 | 4 | Other | 1 | 780 | 4 | Male | 63 | 3 | 1 | Research Scientist | 4 | Single | 2686 | 5207 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 10 | 2 | 2 | 10 | 9 | 7 | 8 |
| 25 | No | Travel_Rarely | 883 | Sales | 26 | 1 | Medical | 1 | 781 | 3 | Female | 32 | 3 | 2 | Sales Executive | 4 | Single | 6180 | 22807 | 1 | Y | No | 23 | 4 | 2 | 80 | 0 | 6 | 5 | 2 | 6 | 5 | 1 | 4 |
| 45 | No | Travel_Rarely | 954 | Sales | 2 | 2 | Technical Degree | 1 | 783 | 2 | Male | 46 | 1 | 2 | Sales Representative | 3 | Single | 6632 | 12388 | 0 | Y | No | 13 | 3 | 1 | 80 | 0 | 9 | 3 | 3 | 8 | 7 | 3 | 1 |
| 23 | No | Travel_Rarely | 310 | Research & Development | 10 | 1 | Medical | 1 | 784 | 1 | Male | 79 | 4 | 1 | Research Scientist | 3 | Single | 3505 | 19630 | 1 | Y | No | 18 | 3 | 4 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 0 | 2 |
| 47 | NA | Travel_Frequently | 719 | Sales | 27 | 2 | Life Sciences | 1 | 785 | 2 | Female | 77 | 4 | 2 | Sales Executive | 3 | Single | 6397 | 10339 | 4 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 8 | 2 | 3 | 5 | 4 | 1 | 3 |
| 34 | No | Travel_Rarely | 304 | Sales | 2 | 3 | Other | 1 | 786 | 4 | Male | 60 | 3 | 2 | Sales Executive | 4 | Single | 6274 | 18686 | 1 | Y | No | 22 | 4 | 3 | 80 | 0 | 6 | 5 | 3 | 6 | 5 | 1 | 4 |
| 55 | Yes | Travel_Rarely | 725 | Research & Development | 2 | 3 | Medical | 1 | 787 | 4 | Male | 78 | 3 | 5 | Manager | 1 | Married | 19859 | 21199 | 5 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 24 | 2 | 3 | 5 | 2 | 1 | 4 |
| 36 | No | Non-Travel | 1434 | Sales | 8 | 4 | Life Sciences | 1 | 789 | 1 | Male | 76 | 2 | 3 | Sales Executive | 1 | Single | 7587 | 14229 | 1 | Y | No | 15 | 3 | 2 | 80 | 0 | 10 | 1 | 3 | 10 | 7 | 0 | 9 |
| 52 | No | Non-Travel | 715 | Research & Development | 19 | 4 | Medical | 1 | 791 | 4 | Male | 41 | 3 | 1 | Research Scientist | 4 | Married | 4258 | 26589 | 0 | Y | No | 18 | 3 | 1 | 80 | 1 | 5 | 3 | 3 | 4 | 3 | 1 | 2 |
| 26 | No | Travel_Frequently | 575 | Research & Development | 1 | 2 | Life Sciences | 1 | 792 | 1 | Female | 71 | 1 | 1 | Laboratory Technician | 4 | Divorced | 4364 | 5288 | 3 | Y | No | 14 | 3 | 1 | 80 | 1 | 5 | 2 | 3 | 2 | 2 | 2 | 0 |
| 29 | NA | Travel_Rarely | 657 | Research & Development | 27 | 3 | Medical | 1 | 793 | 2 | Female | 66 | 3 | 2 | Healthcare Representative | 3 | Married | 4335 | 25549 | 4 | Y | No | 12 | 3 | 1 | 80 | 1 | 11 | 3 | 2 | 8 | 7 | 1 | 1 |
| 26 | Yes | Travel_Rarely | 1146 | Sales | 8 | 3 | Technical Degree | 1 | 796 | 4 | Male | 38 | 2 | 2 | Sales Executive | 1 | Single | 5326 | 3064 | 6 | Y | No | 17 | 3 | 3 | 80 | 0 | 6 | 2 | 2 | 4 | 3 | 1 | 2 |
| 34 | No | Travel_Rarely | 182 | Research & Development | 1 | 4 | Life Sciences | 1 | 797 | 2 | Female | 72 | 4 | 1 | Research Scientist | 4 | Single | 3280 | 13551 | 2 | Y | No | 16 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 4 | 2 | 1 | 3 |
| 54 | NA | Travel_Rarely | 376 | Research & Development | 19 | 4 | Medical | 1 | 799 | 4 | Female | 95 | 3 | 2 | Manufacturing Director | 1 | Divorced | 5485 | 22670 | 9 | Y | Yes | 11 | 3 | 2 | 80 | 2 | 9 | 4 | 3 | 5 | 3 | 1 | 4 |
| 27 | No | Travel_Frequently | 829 | Sales | 8 | 1 | Marketing | 1 | 800 | 3 | Male | 84 | 3 | 2 | Sales Executive | 4 | Married | 4342 | 24008 | 0 | Y | No | 19 | 3 | 2 | 80 | 1 | 5 | 3 | 3 | 4 | 2 | 1 | 1 |
| 37 | No | Travel_Rarely | 571 | Research & Development | 10 | 1 | Life Sciences | 1 | 802 | 4 | Female | 82 | 3 | 1 | Research Scientist | 1 | Divorced | 2782 | 19905 | 0 | Y | Yes | 13 | 3 | 2 | 80 | 2 | 6 | 3 | 2 | 5 | 3 | 4 | 3 |
| 38 | No | Travel_Frequently | 240 | Research & Development | 2 | 4 | Life Sciences | 1 | 803 | 1 | Female | 75 | 4 | 2 | Manufacturing Director | 1 | Single | 5980 | 26085 | 6 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 17 | 2 | 3 | 15 | 7 | 4 | 12 |
| NA | No | Travel_Rarely | 121 | Research & Development | 2 | 4 | Medical | 1 | 804 | 3 | Female | 86 | 2 | 1 | Research Scientist | 1 | Single | 4381 | 7530 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 6 | 5 | 1 | 3 |
| 35 | No | Travel_Rarely | 384 | Sales | 8 | 4 | Life Sciences | 1 | 805 | 1 | Female | 72 | 3 | 1 | Sales Representative | 4 | Married | 2572 | 20317 | 1 | Y | No | 16 | 3 | 2 | 80 | 1 | 3 | 1 | 2 | 3 | 2 | 0 | 2 |
| 30 | No | Travel_Rarely | 921 | Research & Development | 1 | 3 | Life Sciences | 1 | 806 | 4 | Male | 38 | 1 | 1 | Laboratory Technician | 3 | Married | 3833 | 24375 | 3 | Y | No | 21 | 4 | 3 | 80 | 2 | 7 | 2 | 3 | 2 | 2 | 0 | 2 |
| 40 | No | Travel_Frequently | 791 | Research & Development | 2 | 2 | Medical | 1 | 807 | 3 | Female | 38 | 4 | 2 | Healthcare Representative | 2 | Married | 4244 | 9931 | 1 | Y | No | 24 | 4 | 4 | 80 | 1 | 8 | 2 | 3 | 8 | 7 | 3 | 7 |
| 34 | No | Travel_Rarely | 1111 | Sales | 8 | 2 | Life Sciences | 1 | 808 | 3 | Female | 93 | 3 | 2 | Sales Executive | 1 | Married | 6500 | 13305 | 5 | Y | No | 17 | 3 | 2 | 80 | 1 | 6 | 1 | 3 | 3 | 2 | 1 | 2 |
| 42 | No | Travel_Frequently | 570 | Research & Development | 8 | 3 | Life Sciences | 1 | 809 | 2 | Male | 66 | 3 | 5 | Manager | 4 | Divorced | 18430 | 16225 | 1 | Y | No | 13 | 3 | 2 | 80 | 1 | 24 | 4 | 2 | 24 | 7 | 14 | 9 |
| 23 | Yes | Travel_Rarely | 1243 | Research & Development | 6 | 3 | Life Sciences | 1 | 811 | 3 | Male | 63 | 4 | 1 | Laboratory Technician | 1 | Married | 1601 | 3445 | 1 | Y | Yes | 21 | 4 | 3 | 80 | 2 | 1 | 2 | 3 | 0 | 0 | 0 | 0 |
| 24 | No | Non-Travel | 1092 | Research & Development | 9 | 3 | Life Sciences | 1 | 812 | 3 | Male | 60 | 2 | 1 | Laboratory Technician | 2 | Divorced | 2694 | 26551 | 1 | Y | No | 11 | 3 | 3 | 80 | 3 | 1 | 4 | 3 | 1 | 0 | 0 | 0 |
| 52 | No | Travel_Rarely | 1325 | Research & Development | 11 | 4 | Life Sciences | 1 | 813 | 4 | Female | 82 | 3 | 2 | Laboratory Technician | 3 | Married | 3149 | 21821 | 8 | Y | No | 20 | 4 | 2 | 80 | 1 | 9 | 3 | 3 | 5 | 2 | 1 | 4 |
| 50 | NA | Travel_Rarely | 691 | Research & Development | 2 | 3 | Medical | 1 | 815 | 3 | Male | 64 | 3 | 4 | Research Director | 3 | Married | 17639 | 6881 | 5 | Y | No | 16 | 3 | 4 | 80 | 0 | 30 | 3 | 3 | 4 | 3 | 0 | 3 |
| 29 | Yes | Travel_Rarely | 805 | Research & Development | 1 | 2 | Life Sciences | 1 | 816 | 2 | Female | 36 | 2 | 1 | Laboratory Technician | 1 | Married | 2319 | 6689 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 1 | 1 | 1 | 3 | 1 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 213 | Research & Development | 7 | 3 | Medical | 1 | 817 | 3 | Male | 49 | 3 | 3 | Research Director | 3 | Married | 11691 | 25995 | 0 | Y | No | 11 | 3 | 4 | 80 | 0 | 14 | 3 | 4 | 13 | 9 | 3 | 7 |
| 33 | Yes | Travel_Rarely | 118 | Sales | 16 | 3 | Marketing | 1 | 819 | 1 | Female | 69 | 3 | 2 | Sales Executive | 1 | Single | 5324 | 26507 | 5 | Y | No | 15 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 3 | 2 | 0 | 2 |
| NA | No | Travel_Rarely | 202 | Research & Development | 2 | 2 | Other | 1 | 820 | 3 | Female | 33 | 3 | 4 | Manager | 4 | Married | 16752 | 12982 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 26 | 3 | 2 | 26 | 14 | 3 | 0 |
| 36 | No | Travel_Rarely | 676 | Research & Development | 1 | 3 | Other | 1 | 823 | 3 | Female | 35 | 3 | 2 | Manufacturing Director | 2 | Married | 5228 | 23361 | 0 | Y | No | 15 | 3 | 1 | 80 | 1 | 10 | 2 | 3 | 9 | 7 | 0 | 5 |
| 29 | No | Travel_Rarely | 1252 | Research & Development | 23 | 2 | Life Sciences | 1 | 824 | 3 | Male | 81 | 4 | 1 | Research Scientist | 3 | Married | 2700 | 23779 | 1 | Y | No | 24 | 4 | 3 | 80 | 1 | 10 | 3 | 3 | 10 | 7 | 0 | 7 |
| 58 | Yes | Travel_Rarely | 286 | Research & Development | 2 | 4 | Life Sciences | 1 | 825 | 4 | Male | 31 | 3 | 5 | Research Director | 2 | Single | 19246 | 25761 | 7 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 40 | 2 | 3 | 31 | 15 | 13 | 8 |
| 35 | No | Travel_Rarely | 1258 | Research & Development | 1 | 4 | Life Sciences | 1 | 826 | 4 | Female | 40 | 4 | 1 | Research Scientist | 3 | Single | NA | 13301 | 3 | Y | No | 13 | 3 | 3 | 80 | 0 | 7 | 0 | 3 | 2 | 2 | 2 | 2 |
| 42 | No | Travel_Rarely | 932 | Research & Development | 1 | 2 | Life Sciences | 1 | 827 | 4 | Female | 43 | 2 | 2 | Manufacturing Director | 4 | Married | NA | 4051 | 9 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 8 | 4 | 3 | 4 | 3 | 0 | 2 |
| 28 | Yes | Travel_Rarely | 890 | Research & Development | 2 | 4 | Medical | 1 | 828 | 3 | Male | 46 | 3 | 1 | Research Scientist | 3 | Single | NA | 16374 | 6 | Y | No | 17 | 3 | 4 | 80 | 0 | 5 | 3 | 2 | 2 | 2 | 2 | 1 |
| 36 | No | Travel_Rarely | 1041 | Human Resources | 13 | 3 | Human Resources | 1 | 829 | 3 | Male | 36 | 3 | 1 | Human Resources | 2 | Married | 2143 | 25527 | 4 | Y | No | 13 | 3 | 2 | 80 | 1 | 8 | 2 | 3 | 5 | 2 | 0 | 4 |
| NA | No | Travel_Rarely | 859 | Research & Development | 4 | 3 | Life Sciences | 1 | 830 | 3 | Female | 98 | 2 | 2 | Manufacturing Director | 3 | Married | 6162 | 19124 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 14 | 3 | 3 | 14 | 13 | 6 | 8 |
| 40 | No | Travel_Frequently | 720 | Research & Development | 16 | 4 | Medical | 1 | 832 | 1 | Male | 51 | 2 | 2 | Laboratory Technician | 3 | Single | 5094 | 11983 | 6 | Y | No | 14 | 3 | 4 | 80 | 0 | 10 | 6 | 3 | 1 | 0 | 0 | 0 |
| NA | NA | Travel_Rarely | 946 | Research & Development | 2 | 3 | Medical | 1 | 833 | 3 | Female | 52 | 2 | 2 | Manufacturing Director | 4 | Single | 6877 | 20234 | 5 | Y | Yes | 24 | 4 | 2 | 80 | 0 | 12 | 4 | 2 | 0 | 0 | 0 | 0 |
| 45 | No | Travel_Rarely | 252 | Research & Development | 2 | 3 | Life Sciences | 1 | 834 | 2 | Female | 95 | 2 | 1 | Research Scientist | 3 | Single | 2274 | 6153 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 42 | No | Travel_Rarely | 933 | Research & Development | 29 | 3 | Life Sciences | 1 | 836 | 2 | Male | 98 | 3 | 2 | Manufacturing Director | 2 | Married | NA | 11806 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 10 | 3 | 2 | 9 | 8 | 7 | 8 |
| 38 | NA | Travel_Frequently | 471 | Research & Development | 12 | 3 | Life Sciences | 1 | 837 | 1 | Male | 45 | 2 | 2 | Healthcare Representative | 1 | Divorced | NA | 4284 | 2 | Y | No | 15 | 3 | 3 | 80 | 1 | 13 | 3 | 2 | 4 | 3 | 1 | 2 |
| 34 | No | Travel_Frequently | 702 | Research & Development | 16 | 4 | Life Sciences | 1 | 838 | 3 | Female | 100 | 2 | 1 | Research Scientist | 4 | Single | 2553 | 8306 | 1 | Y | No | 16 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 5 | 2 | 1 | 3 |
| 49 | Yes | Travel_Rarely | 1184 | Sales | 11 | 3 | Marketing | 1 | 840 | 3 | Female | 43 | 3 | 3 | Sales Executive | 4 | Married | 7654 | 5860 | 1 | Y | No | 18 | 3 | 1 | 80 | 2 | 9 | 3 | 4 | 9 | 8 | 7 | 7 |
| 55 | Yes | Travel_Rarely | 436 | Sales | 2 | 1 | Medical | 1 | 842 | 3 | Male | 37 | 3 | 2 | Sales Executive | 4 | Single | 5160 | 21519 | 4 | Y | No | 16 | 3 | 3 | 80 | 0 | 12 | 3 | 2 | 9 | 7 | 7 | 3 |
| 43 | NA | Travel_Rarely | 589 | Research & Development | 14 | 2 | Life Sciences | 1 | 843 | 2 | Male | 94 | 3 | 4 | Research Director | 1 | Married | 17159 | 5200 | 6 | Y | No | 24 | 4 | 3 | 80 | 1 | 22 | 3 | 3 | 4 | 1 | 1 | 0 |
| 27 | No | Travel_Rarely | 269 | Research & Development | 5 | 1 | Technical Degree | 1 | 844 | 3 | Male | 42 | 2 | 3 | Research Director | 4 | Divorced | 12808 | 8842 | 1 | Y | Yes | 16 | 3 | 2 | 80 | 1 | 9 | 3 | 3 | 9 | 8 | 0 | 8 |
| 35 | No | Travel_Rarely | 950 | Research & Development | 7 | 3 | Other | 1 | 845 | 3 | Male | 59 | 3 | 3 | Manufacturing Director | 3 | Single | 10221 | 18869 | 3 | Y | No | 21 | 4 | 2 | 80 | 0 | 17 | 3 | 4 | 8 | 5 | 1 | 6 |
| 28 | No | Travel_Rarely | 760 | Sales | 2 | 4 | Marketing | 1 | 846 | 2 | Female | 81 | 3 | 2 | Sales Executive | 2 | Married | 4779 | 3698 | 1 | Y | Yes | 20 | 4 | 1 | 80 | 0 | 8 | 2 | 3 | 8 | 7 | 7 | 5 |
| 34 | No | Travel_Rarely | 829 | Human Resources | 3 | 2 | Human Resources | 1 | 847 | 3 | Male | 88 | 3 | 1 | Human Resources | 4 | Married | 3737 | 2243 | 0 | Y | No | 19 | 3 | 3 | 80 | 1 | 4 | 1 | 1 | 3 | 2 | 0 | 2 |
| 26 | Yes | Travel_Frequently | 887 | Research & Development | 5 | 2 | Medical | 1 | 848 | 3 | Female | 88 | 2 | 1 | Research Scientist | 3 | Married | 2366 | 20898 | 1 | Y | Yes | 14 | 3 | 1 | 80 | 1 | 8 | 2 | 3 | 8 | 7 | 1 | 7 |
| 27 | No | Non-Travel | 443 | Research & Development | 3 | 3 | Medical | 1 | 850 | 4 | Male | 50 | 3 | 1 | Research Scientist | 4 | Married | 1706 | 16571 | 1 | Y | No | 11 | 3 | 3 | 80 | 3 | 0 | 6 | 2 | 0 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 1318 | Sales | 26 | 4 | Marketing | 1 | 851 | 1 | Female | 66 | 3 | 4 | Manager | 3 | Married | 16307 | 5594 | 2 | Y | No | 14 | 3 | 3 | 80 | 1 | 29 | 2 | 2 | 20 | 6 | 4 | 17 |
| 44 | No | Travel_Rarely | 625 | Research & Development | 4 | 3 | Medical | 1 | 852 | 4 | Male | 50 | 3 | 2 | Healthcare Representative | 2 | Single | 5933 | 5197 | 9 | Y | No | 12 | 3 | 4 | 80 | 0 | 10 | 2 | 2 | 5 | 2 | 2 | 3 |
| 25 | No | Travel_Rarely | 180 | Research & Development | 2 | 1 | Medical | 1 | 854 | 1 | Male | 65 | 4 | 1 | Research Scientist | 1 | Single | 3424 | 21632 | 7 | Y | No | 13 | 3 | 3 | 80 | 0 | 6 | 3 | 2 | 4 | 3 | 0 | 1 |
| 33 | No | Travel_Rarely | 586 | Sales | 1 | 3 | Medical | 1 | 855 | 1 | Male | 48 | 4 | 2 | Sales Executive | 1 | Divorced | 4037 | 21816 | 1 | Y | No | 22 | 4 | 1 | 80 | 1 | 9 | 5 | 3 | 9 | 8 | 0 | 8 |
| 35 | NA | Travel_Rarely | 1343 | Research & Development | 27 | 1 | Medical | 1 | 856 | 3 | Female | 53 | 2 | 1 | Research Scientist | 1 | Single | 2559 | 17852 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 6 | 3 | 2 | 6 | 5 | 1 | 1 |
| 36 | No | Travel_Rarely | 928 | Sales | 1 | 2 | Life Sciences | 1 | 857 | 2 | Male | 56 | 3 | 2 | Sales Executive | 4 | Married | 6201 | 2823 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 1 | 18 | 1 | 2 | 18 | 14 | 4 | 11 |
| 32 | No | Travel_Rarely | 117 | Sales | 13 | 4 | Life Sciences | 1 | 859 | 2 | Male | 73 | 3 | 2 | Sales Executive | 4 | Divorced | 4403 | 9250 | 2 | Y | No | 11 | 3 | 3 | 80 | 1 | 8 | 3 | 2 | 5 | 2 | 0 | 3 |
| 30 | No | Travel_Frequently | 1012 | Research & Development | 5 | 4 | Life Sciences | 1 | 861 | 2 | Male | 75 | 2 | 1 | Research Scientist | 4 | Divorced | 3761 | 2373 | 9 | Y | No | 12 | 3 | 2 | 80 | 1 | 10 | 3 | 2 | 5 | 4 | 0 | 3 |
| 53 | No | Travel_Rarely | 661 | Sales | 7 | 2 | Marketing | 1 | 862 | 1 | Female | 78 | 2 | 3 | Sales Executive | 4 | Married | 10934 | 20715 | 7 | Y | Yes | 18 | 3 | 4 | 80 | 1 | 35 | 3 | 3 | 5 | 2 | 0 | 4 |
| 45 | No | Travel_Rarely | 930 | Sales | 9 | 3 | Marketing | 1 | 864 | 4 | Male | 74 | 3 | 3 | Sales Executive | 1 | Divorced | 10761 | 19239 | 4 | Y | Yes | 12 | 3 | 3 | 80 | 1 | 18 | 2 | 3 | 5 | 4 | 0 | 2 |
| 32 | No | Travel_Rarely | 638 | Research & Development | 8 | 2 | Medical | 1 | 865 | 3 | Female | 91 | 4 | 2 | Research Scientist | 3 | Married | 5175 | 22162 | 5 | Y | No | 12 | 3 | 3 | 80 | 1 | 9 | 3 | 2 | 5 | 3 | 1 | 3 |
| NA | No | Travel_Frequently | 890 | Research & Development | 25 | 4 | Medical | 1 | 867 | 3 | Female | 81 | 2 | 4 | Manufacturing Director | 4 | Married | NA | 19028 | 3 | Y | No | 22 | 4 | 3 | 80 | 0 | 31 | 3 | 3 | 9 | 8 | 0 | 0 |
| 37 | No | Travel_Rarely | 342 | Sales | 16 | 4 | Marketing | 1 | 868 | 4 | Male | 66 | 2 | 2 | Sales Executive | 3 | Divorced | NA | 24558 | 4 | Y | No | 19 | 3 | 4 | 80 | 2 | 9 | 2 | 3 | 1 | 0 | 0 | 0 |
| 28 | No | Travel_Rarely | 1169 | Human Resources | 8 | 2 | Medical | 1 | 869 | 2 | Male | 63 | 2 | 1 | Human Resources | 4 | Divorced | 4936 | 23965 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 6 | 6 | 3 | 5 | 1 | 0 | 4 |
| NA | No | Travel_Rarely | 1230 | Research & Development | 1 | 2 | Life Sciences | 1 | 872 | 4 | Male | 33 | 2 | 2 | Manufacturing Director | 4 | Married | 4775 | 19146 | 6 | Y | No | 22 | 4 | 1 | 80 | 2 | 4 | 2 | 1 | 2 | 2 | 2 | 2 |
| 44 | No | Travel_Rarely | 986 | Research & Development | 8 | 4 | Life Sciences | 1 | 874 | 1 | Male | 62 | 4 | 1 | Laboratory Technician | 4 | Married | 2818 | 5044 | 2 | Y | Yes | 24 | 4 | 3 | 80 | 1 | 10 | 2 | 2 | 3 | 2 | 0 | 2 |
| 42 | No | Travel_Frequently | 1271 | Research & Development | 2 | 1 | Medical | 1 | 875 | 2 | Male | 35 | 3 | 1 | Research Scientist | 4 | Single | 2515 | 9068 | 5 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 8 | 2 | 3 | 2 | 1 | 2 | 2 |
| 36 | No | Travel_Rarely | 1278 | Human Resources | 8 | 3 | Life Sciences | 1 | 878 | 1 | Male | 77 | 2 | 1 | Human Resources | 1 | Married | 2342 | 8635 | 0 | Y | No | 21 | 4 | 3 | 80 | 0 | 6 | 3 | 3 | 5 | 4 | 0 | 3 |
| 25 | No | Travel_Rarely | 141 | Sales | 3 | 1 | Other | 1 | 879 | 3 | Male | 98 | 3 | 2 | Sales Executive | 1 | Married | NA | 14363 | 1 | Y | Yes | 18 | 3 | 4 | 80 | 0 | 5 | 3 | 3 | 5 | 3 | 0 | 3 |
| 35 | No | Travel_Rarely | 607 | Research & Development | 9 | 3 | Life Sciences | 1 | 880 | 4 | Female | 66 | 2 | 3 | Manufacturing Director | 3 | Married | 10685 | 23457 | 1 | Y | Yes | 20 | 4 | 2 | 80 | 1 | 17 | 2 | 3 | 17 | 14 | 5 | 15 |
| 35 | Yes | Travel_Frequently | 130 | Research & Development | 25 | 4 | Life Sciences | 1 | 881 | 4 | Female | 96 | 3 | 1 | Research Scientist | 2 | Divorced | 2022 | 16612 | 1 | Y | Yes | 19 | 3 | 1 | 80 | 1 | 10 | 3 | 2 | 10 | 2 | 7 | 8 |
| 32 | No | Non-Travel | 300 | Research & Development | 1 | 3 | Life Sciences | 1 | 882 | 4 | Male | 61 | 3 | 1 | Laboratory Technician | 4 | Divorced | 2314 | 9148 | 0 | Y | No | 12 | 3 | 2 | 80 | 1 | 4 | 2 | 3 | 3 | 0 | 0 | 2 |
| 25 | No | Travel_Rarely | 583 | Sales | 4 | 1 | Marketing | 1 | 885 | 3 | Male | 87 | 2 | 2 | Sales Executive | 1 | Married | 4256 | 18154 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 5 | 1 | 4 | 5 | 2 | 0 | 3 |
| 49 | No | Travel_Rarely | 1418 | Research & Development | 1 | 3 | Technical Degree | 1 | 887 | 3 | Female | 36 | 3 | 1 | Research Scientist | 1 | Married | 3580 | 10554 | 2 | Y | No | 16 | 3 | 2 | 80 | 1 | 7 | 2 | 3 | 4 | 2 | 0 | 2 |
| NA | No | Non-Travel | 1269 | Research & Development | 4 | 1 | Life Sciences | 1 | 888 | 1 | Male | 46 | 2 | 1 | Laboratory Technician | 4 | Married | 3162 | 10778 | 0 | Y | No | 17 | 3 | 4 | 80 | 0 | 6 | 2 | 2 | 5 | 2 | 3 | 4 |
| 32 | No | Travel_Frequently | 379 | Sales | 5 | 2 | Life Sciences | 1 | 889 | 2 | Male | 48 | 3 | 2 | Sales Executive | 2 | Married | 6524 | 8891 | 1 | Y | No | 14 | 3 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 8 | 5 | 3 |
| 38 | NA | Travel_Rarely | 395 | Sales | 9 | 3 | Marketing | 1 | 893 | 2 | Male | 98 | 2 | 1 | Sales Representative | 2 | Married | 2899 | 12102 | 0 | Y | No | 19 | 3 | 4 | 80 | 1 | 3 | 3 | 3 | 2 | 2 | 1 | 2 |
| 42 | No | Travel_Rarely | 1265 | Research & Development | 3 | 3 | Life Sciences | 1 | 894 | 3 | Female | 95 | 4 | 2 | Laboratory Technician | 4 | Married | 5231 | 23726 | 2 | Y | Yes | 13 | 3 | 2 | 80 | 1 | 17 | 1 | 2 | 5 | 3 | 1 | 3 |
| 31 | No | Travel_Rarely | 1222 | Research & Development | 11 | 4 | Life Sciences | 1 | 895 | 4 | Male | 48 | 3 | 1 | Research Scientist | 4 | Married | 2356 | 14871 | 3 | Y | Yes | 19 | 3 | 2 | 80 | 1 | 8 | 2 | 3 | 6 | 4 | 0 | 2 |
| 29 | NA | Travel_Rarely | 341 | Sales | 1 | 3 | Medical | 1 | 896 | 2 | Female | 48 | 2 | 1 | Sales Representative | 3 | Divorced | 2800 | 23522 | 6 | Y | Yes | 19 | 3 | 3 | 80 | 3 | 5 | 3 | 3 | 3 | 2 | 0 | 2 |
| 53 | No | Travel_Rarely | 868 | Sales | 8 | 3 | Marketing | 1 | 897 | 1 | Male | 73 | 3 | 4 | Sales Executive | 4 | Married | 11836 | 22789 | 5 | Y | No | 14 | 3 | 3 | 80 | 1 | 28 | 3 | 3 | 2 | 0 | 2 | 2 |
| 35 | NA | Travel_Rarely | 672 | Research & Development | 25 | 3 | Technical Degree | 1 | 899 | 4 | Male | 78 | 2 | 3 | Manufacturing Director | 2 | Married | 10903 | 9129 | 3 | Y | No | 16 | 3 | 1 | 80 | 0 | 16 | 2 | 3 | 13 | 10 | 4 | 8 |
| 37 | No | Travel_Frequently | 1231 | Sales | 21 | 2 | Medical | 1 | 900 | 3 | Female | 54 | 3 | 1 | Sales Representative | 4 | Married | 2973 | 21222 | 5 | Y | No | 15 | 3 | 2 | 80 | 1 | 10 | 3 | 3 | 5 | 4 | 0 | 0 |
| 53 | No | Travel_Rarely | 102 | Research & Development | 23 | 4 | Life Sciences | 1 | 901 | 4 | Female | 72 | 3 | 4 | Research Director | 4 | Single | NA | 20206 | 6 | Y | No | 18 | 3 | 3 | 80 | 0 | 33 | 0 | 3 | 12 | 9 | 3 | 8 |
| 43 | NA | Travel_Frequently | 422 | Research & Development | 1 | 3 | Life Sciences | 1 | 902 | 4 | Female | 33 | 3 | 2 | Healthcare Representative | 4 | Married | 5562 | 21782 | 4 | Y | No | 13 | 3 | 2 | 80 | 1 | 12 | 2 | 2 | 5 | 2 | 2 | 2 |
| 47 | No | Travel_Rarely | 249 | Sales | 2 | 2 | Marketing | 1 | 903 | 3 | Female | 35 | 3 | 2 | Sales Executive | 4 | Married | 4537 | 17783 | 0 | Y | Yes | 22 | 4 | 1 | 80 | 1 | 8 | 2 | 3 | 7 | 6 | 7 | 7 |
| 37 | No | Non-Travel | 1252 | Sales | 19 | 2 | Medical | 1 | 904 | 1 | Male | 32 | 3 | 3 | Sales Executive | 2 | Single | 7642 | 4814 | 1 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 10 | 0 | 0 | 9 |
| 50 | No | Non-Travel | 881 | Research & Development | 2 | 4 | Life Sciences | 1 | 905 | 1 | Male | 98 | 3 | 4 | Manager | 1 | Divorced | 17924 | 4544 | 1 | Y | No | 11 | 3 | 4 | 80 | 1 | 31 | 3 | 3 | 31 | 6 | 14 | 7 |
| 39 | No | Travel_Rarely | 1383 | Human Resources | 2 | 3 | Life Sciences | 1 | 909 | 4 | Female | 42 | 2 | 2 | Human Resources | 4 | Married | 5204 | 7790 | 8 | Y | No | 11 | 3 | 3 | 80 | 2 | 13 | 2 | 3 | 5 | 4 | 0 | 4 |
| 33 | No | Travel_Rarely | 1075 | Human Resources | 3 | 2 | Human Resources | 1 | 910 | 4 | Male | 57 | 3 | 1 | Human Resources | 2 | Divorced | 2277 | 22650 | 3 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 7 | 4 | 4 | 4 | 3 | 0 | 3 |
| 32 | Yes | Travel_Rarely | 374 | Research & Development | 25 | 4 | Life Sciences | 1 | 911 | 1 | Male | 87 | 3 | 1 | Laboratory Technician | 4 | Single | NA | 18016 | 1 | Y | Yes | 24 | 4 | 3 | 80 | 0 | 1 | 2 | 1 | 1 | 0 | 0 | 1 |
| 29 | No | Travel_Rarely | 1086 | Research & Development | 7 | 1 | Medical | 1 | 912 | 1 | Female | 62 | 2 | 1 | Laboratory Technician | 4 | Divorced | 2532 | 6054 | 6 | Y | No | 14 | 3 | 3 | 80 | 3 | 8 | 5 | 3 | 4 | 3 | 0 | 3 |
| 44 | NA | Travel_Rarely | 661 | Research & Development | 9 | 2 | Life Sciences | 1 | 913 | 2 | Male | 61 | 3 | 1 | Research Scientist | 1 | Married | NA | 7508 | 1 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 8 | 0 | 3 | 8 | 7 | 7 | 1 |
| 28 | No | Travel_Rarely | 821 | Sales | 5 | 4 | Medical | 1 | 916 | 1 | Male | 98 | 3 | 2 | Sales Executive | 4 | Single | 4908 | 24252 | 1 | Y | No | 14 | 3 | 2 | 80 | 0 | 4 | 3 | 3 | 4 | 2 | 0 | 2 |
| 58 | Yes | Travel_Frequently | 781 | Research & Development | 2 | 1 | Life Sciences | 1 | 918 | 4 | Male | 57 | 2 | 1 | Laboratory Technician | 4 | Divorced | 2380 | 13384 | 9 | Y | Yes | 14 | 3 | 4 | 80 | 1 | 3 | 3 | 2 | 1 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 177 | Research & Development | 8 | 3 | Life Sciences | 1 | 920 | 1 | Female | 55 | 3 | 2 | Manufacturing Director | 2 | Divorced | NA | 23814 | 4 | Y | No | 21 | 4 | 3 | 80 | 1 | 4 | 2 | 4 | 1 | 0 | 0 | 0 |
| 20 | Yes | Travel_Rarely | 500 | Sales | 2 | 3 | Medical | 1 | 922 | 3 | Female | 49 | 2 | 1 | Sales Representative | 3 | Single | 2044 | 22052 | 1 | Y | No | 13 | 3 | 4 | 80 | 0 | 2 | 3 | 2 | 2 | 2 | 0 | 2 |
| NA | Yes | Travel_Rarely | 1427 | Research & Development | 18 | 1 | Other | 1 | 923 | 4 | Female | 65 | 3 | 1 | Research Scientist | 4 | Single | 2693 | 8870 | 1 | Y | No | 19 | 3 | 1 | 80 | 0 | 1 | 3 | 2 | 1 | 0 | 0 | 0 |
| 36 | No | Travel_Rarely | 1425 | Research & Development | 14 | 1 | Life Sciences | 1 | 924 | 3 | Male | 68 | 3 | 2 | Healthcare Representative | 4 | Married | 6586 | 4821 | 0 | Y | Yes | 17 | 3 | 1 | 80 | 1 | 17 | 2 | 2 | 16 | 8 | 4 | 11 |
| 47 | No | Travel_Rarely | 1454 | Sales | 2 | 4 | Life Sciences | 1 | 925 | 4 | Female | 65 | 2 | 1 | Sales Representative | 4 | Single | 3294 | 13137 | 1 | Y | Yes | 18 | 3 | 1 | 80 | 0 | 3 | 3 | 2 | 3 | 2 | 1 | 2 |
| 22 | Yes | Travel_Rarely | 617 | Research & Development | 3 | 1 | Life Sciences | 1 | 926 | 2 | Female | 34 | 3 | 2 | Manufacturing Director | 3 | Married | 4171 | 10022 | 0 | Y | Yes | 19 | 3 | 1 | 80 | 1 | 4 | 3 | 4 | 3 | 2 | 0 | 2 |
| NA | Yes | Travel_Rarely | 1085 | Research & Development | 2 | 4 | Life Sciences | 1 | 927 | 2 | Female | 57 | 1 | 1 | Laboratory Technician | 4 | Divorced | 2778 | 17725 | 4 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 10 | 1 | 2 | 7 | 7 | 1 | 0 |
| 28 | No | Travel_Rarely | 995 | Research & Development | 9 | 3 | Medical | 1 | 930 | 3 | Female | 77 | 3 | 1 | Research Scientist | 3 | Divorced | 2377 | 9834 | 5 | Y | No | 18 | 3 | 2 | 80 | 1 | 6 | 2 | 3 | 2 | 2 | 2 | 2 |
| 39 | Yes | Travel_Rarely | 1122 | Research & Development | 6 | 3 | Medical | 1 | 932 | 4 | Male | 70 | 3 | 1 | Laboratory Technician | 1 | Married | 2404 | 4303 | 7 | Y | Yes | 21 | 4 | 4 | 80 | 0 | 8 | 2 | 1 | 2 | 2 | 2 | 2 |
| 27 | No | Travel_Rarely | 618 | Research & Development | 4 | 3 | Life Sciences | 1 | 933 | 2 | Female | 76 | 3 | 1 | Research Scientist | 3 | Single | 2318 | 17808 | 1 | Y | No | 19 | 3 | 3 | 80 | 0 | 1 | 2 | 3 | 1 | 1 | 0 | 0 |
| 34 | No | Travel_Rarely | 546 | Research & Development | 10 | 3 | Life Sciences | 1 | 934 | 2 | Male | 83 | 3 | 1 | Laboratory Technician | 2 | Divorced | NA | 6896 | 1 | Y | No | 14 | 3 | 2 | 80 | 2 | 1 | 3 | 3 | 1 | 0 | 1 | 0 |
| 42 | No | Travel_Rarely | 462 | Sales | 14 | 2 | Medical | 1 | 936 | 3 | Female | 68 | 2 | 2 | Sales Executive | 3 | Single | NA | 7824 | 7 | Y | No | 17 | 3 | 1 | 80 | 0 | 10 | 6 | 3 | 5 | 4 | 0 | 3 |
| 33 | No | Travel_Rarely | 1198 | Research & Development | 1 | 4 | Other | 1 | 939 | 3 | Male | 100 | 2 | 1 | Research Scientist | 1 | Single | 2799 | 3339 | 3 | Y | Yes | 11 | 3 | 2 | 80 | 0 | 6 | 1 | 3 | 3 | 2 | 0 | 2 |
| 58 | NA | Travel_Rarely | 1272 | Research & Development | 5 | 3 | Technical Degree | 1 | 940 | 3 | Female | 37 | 2 | 3 | Healthcare Representative | 2 | Divorced | 10552 | 9255 | 2 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 24 | 3 | 3 | 6 | 0 | 0 | 4 |
| 31 | No | Travel_Rarely | 154 | Sales | 7 | 4 | Life Sciences | 1 | 941 | 2 | Male | 41 | 2 | 1 | Sales Representative | 3 | Married | 2329 | 11737 | 3 | Y | No | 15 | 3 | 2 | 80 | 0 | 13 | 2 | 4 | 7 | 7 | 5 | 2 |
| 35 | No | Travel_Rarely | 1137 | Research & Development | 21 | 1 | Life Sciences | 1 | 942 | 4 | Female | 51 | 3 | 2 | Healthcare Representative | 4 | Married | 4014 | 19170 | 1 | Y | Yes | 25 | 4 | 4 | 80 | 1 | 10 | 2 | 1 | 10 | 6 | 0 | 7 |
| 49 | No | Travel_Rarely | 527 | Research & Development | 8 | 2 | Other | 1 | 944 | 1 | Female | 51 | 3 | 3 | Laboratory Technician | 2 | Married | 7403 | 22477 | 4 | Y | No | 11 | 3 | 3 | 80 | 1 | 29 | 3 | 2 | 26 | 9 | 1 | 7 |
| 48 | NA | Travel_Rarely | 1469 | Research & Development | 20 | 4 | Medical | 1 | 945 | 4 | Male | 51 | 3 | 1 | Research Scientist | 3 | Married | 2259 | 5543 | 4 | Y | No | 17 | 3 | 1 | 80 | 2 | 13 | 2 | 2 | 0 | 0 | 0 | 0 |
| 31 | No | Non-Travel | 1188 | Sales | 20 | 2 | Marketing | 1 | 947 | 4 | Female | 45 | 3 | 2 | Sales Executive | 3 | Married | 6932 | 24406 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 9 | 2 | 2 | 9 | 8 | 0 | 0 |
| 36 | No | Travel_Rarely | 188 | Research & Development | 7 | 4 | Other | 1 | 949 | 2 | Male | 65 | 3 | 1 | Research Scientist | 4 | Single | NA | 23293 | 2 | Y | No | 18 | 3 | 3 | 80 | 0 | 8 | 6 | 3 | 6 | 2 | 0 | 1 |
| 38 | No | Travel_Rarely | 1333 | Research & Development | 1 | 3 | Technical Degree | 1 | 950 | 4 | Female | 80 | 3 | 3 | Research Director | 1 | Married | 13582 | 16292 | 1 | Y | No | 13 | 3 | 2 | 80 | 1 | 15 | 3 | 3 | 15 | 12 | 5 | 11 |
| 32 | No | Non-Travel | 1184 | Research & Development | 1 | 3 | Life Sciences | 1 | 951 | 3 | Female | 70 | 2 | 1 | Laboratory Technician | 2 | Married | 2332 | 3974 | 6 | Y | No | 20 | 4 | 3 | 80 | 0 | 5 | 3 | 3 | 3 | 0 | 0 | 2 |
| 25 | Yes | Travel_Rarely | 867 | Sales | 19 | 2 | Marketing | 1 | 952 | 3 | Male | 36 | 2 | 1 | Sales Representative | 2 | Married | NA | 18798 | 1 | Y | Yes | 18 | 3 | 3 | 80 | 3 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 40 | No | Travel_Rarely | 658 | Sales | 10 | 4 | Marketing | 1 | 954 | 1 | Male | 67 | 2 | 3 | Sales Executive | 2 | Divorced | 9705 | 20652 | 2 | Y | No | 12 | 3 | 2 | 80 | 1 | 11 | 2 | 2 | 1 | 0 | 0 | 0 |
| NA | NA | Travel_Frequently | 1283 | Sales | 1 | 3 | Medical | 1 | 956 | 3 | Male | 52 | 2 | 2 | Sales Executive | 1 | Single | NA | 11148 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 7 | 2 | 3 | 7 | 7 | 0 | 7 |
| 41 | No | Travel_Rarely | 263 | Research & Development | 6 | 3 | Medical | 1 | 957 | 4 | Male | 59 | 3 | 1 | Laboratory Technician | 1 | Single | 4721 | 3119 | 2 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 20 | 3 | 3 | 18 | 13 | 2 | 17 |
| 36 | No | Travel_Rarely | 938 | Research & Development | 2 | 4 | Medical | 1 | 958 | 3 | Male | 79 | 3 | 1 | Laboratory Technician | 3 | Single | 2519 | 12287 | 4 | Y | No | 21 | 4 | 3 | 80 | 0 | 16 | 6 | 3 | 11 | 8 | 3 | 9 |
| 19 | Yes | Travel_Rarely | 419 | Sales | 21 | 3 | Other | 1 | 959 | 4 | Male | 37 | 2 | 1 | Sales Representative | 2 | Single | 2121 | 9947 | 1 | Y | Yes | 13 | 3 | 2 | 80 | 0 | 1 | 3 | 4 | 1 | 0 | 0 | 0 |
| 20 | Yes | Travel_Rarely | 129 | Research & Development | 4 | 3 | Technical Degree | 1 | 960 | 1 | Male | 84 | 3 | 1 | Laboratory Technician | 1 | Single | 2973 | 13008 | 1 | Y | No | 19 | 3 | 2 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 31 | NA | Travel_Rarely | 616 | Research & Development | 12 | 3 | Medical | 1 | 961 | 4 | Female | 41 | 3 | 2 | Healthcare Representative | 4 | Married | 5855 | 17369 | 0 | Y | Yes | 11 | 3 | 3 | 80 | 2 | 10 | 2 | 1 | 9 | 7 | 8 | 5 |
| 40 | No | Travel_Frequently | 1469 | Research & Development | 9 | 4 | Medical | 1 | 964 | 4 | Male | 35 | 3 | 1 | Research Scientist | 2 | Divorced | NA | 25063 | 8 | Y | Yes | 14 | 3 | 4 | 80 | 1 | 3 | 2 | 3 | 1 | 1 | 0 | 0 |
| 32 | No | Travel_Rarely | 498 | Research & Development | 3 | 4 | Medical | 1 | 966 | 3 | Female | 93 | 3 | 2 | Manufacturing Director | 1 | Married | 6725 | 13554 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 8 | 2 | 4 | 8 | 7 | 6 | 3 |
| 36 | Yes | Travel_Rarely | 530 | Sales | 3 | 1 | Life Sciences | 1 | 967 | 3 | Male | 51 | 2 | 3 | Sales Executive | 4 | Married | NA | 5518 | 1 | Y | Yes | 11 | 3 | 1 | 80 | 1 | 16 | 6 | 3 | 16 | 7 | 3 | 7 |
| 33 | No | Travel_Rarely | 1069 | Research & Development | 1 | 3 | Life Sciences | 1 | 969 | 2 | Female | 42 | 2 | 2 | Healthcare Representative | 4 | Single | 6949 | 12291 | 0 | Y | No | 14 | 3 | 1 | 80 | 0 | 6 | 3 | 3 | 5 | 0 | 1 | 4 |
| 37 | Yes | Travel_Rarely | 625 | Sales | 1 | 4 | Life Sciences | 1 | 970 | 1 | Male | 46 | 2 | 3 | Sales Executive | 3 | Married | 10609 | 14922 | 5 | Y | No | 11 | 3 | 3 | 80 | 0 | 17 | 2 | 1 | 14 | 1 | 11 | 7 |
| 45 | No | Non-Travel | 805 | Research & Development | 4 | 2 | Life Sciences | 1 | 972 | 3 | Male | 57 | 3 | 2 | Laboratory Technician | 2 | Married | 4447 | 23163 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 9 | 5 | 2 | 9 | 7 | 0 | 8 |
| 29 | No | Travel_Frequently | 1404 | Sales | 20 | 3 | Technical Degree | 1 | 974 | 3 | Female | 84 | 3 | 1 | Sales Representative | 4 | Married | 2157 | 18203 | 1 | Y | No | 15 | 3 | 2 | 80 | 1 | 3 | 5 | 3 | 3 | 1 | 0 | 2 |
| 35 | No | Travel_Rarely | 1219 | Sales | 18 | 3 | Medical | 1 | 975 | 3 | Female | 86 | 3 | 2 | Sales Executive | 3 | Married | 4601 | 6179 | 1 | Y | No | 16 | 3 | 2 | 80 | 0 | 5 | 3 | 3 | 5 | 2 | 1 | 0 |
| 52 | No | Travel_Rarely | 1053 | Research & Development | 1 | 2 | Life Sciences | 1 | 976 | 4 | Male | 70 | 3 | 4 | Manager | 4 | Married | 17099 | 13829 | 2 | Y | No | 15 | 3 | 2 | 80 | 1 | 26 | 2 | 2 | 9 | 8 | 7 | 8 |
| 58 | Yes | Travel_Rarely | 289 | Research & Development | 2 | 3 | Technical Degree | 1 | 977 | 4 | Male | 51 | 3 | 1 | Research Scientist | 3 | Single | NA | 26227 | 4 | Y | No | 24 | 4 | 1 | 80 | 0 | 7 | 4 | 3 | 1 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 1376 | Sales | 2 | 2 | Medical | 1 | 981 | 3 | Male | 45 | 3 | 4 | Manager | 3 | Divorced | NA | 13938 | 6 | Y | No | 13 | 3 | 3 | 80 | 1 | 22 | 3 | 4 | 17 | 13 | 15 | 2 |
| 30 | No | Travel_Rarely | 231 | Sales | 8 | 2 | Other | 1 | 982 | 3 | Male | 62 | 3 | 3 | Sales Executive | 3 | Divorced | 7264 | 9977 | 5 | Y | No | 11 | 3 | 1 | 80 | 1 | 10 | 2 | 4 | 8 | 4 | 7 | 7 |
| 38 | No | Non-Travel | 152 | Sales | 10 | 3 | Technical Degree | 1 | 983 | 3 | Female | 85 | 3 | 2 | Sales Executive | 4 | Single | NA | 19899 | 1 | Y | Yes | 13 | 3 | 2 | 80 | 0 | 6 | 1 | 3 | 5 | 3 | 1 | 3 |
| 35 | No | Travel_Rarely | 882 | Sales | 3 | 4 | Life Sciences | 1 | 984 | 4 | Male | 92 | 3 | 3 | Sales Executive | 4 | Divorced | 7823 | 6812 | 6 | Y | No | 13 | 3 | 2 | 80 | 1 | 12 | 2 | 3 | 10 | 9 | 0 | 8 |
| 39 | No | Travel_Rarely | 903 | Sales | 2 | 5 | Life Sciences | 1 | 985 | 1 | Male | 41 | 4 | 3 | Sales Executive | 3 | Single | 7880 | 2560 | 0 | Y | No | 18 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 8 | 7 | 0 | 7 |
| 40 | Yes | Non-Travel | 1479 | Sales | 24 | 3 | Life Sciences | 1 | 986 | 2 | Female | 100 | 4 | 4 | Sales Executive | 2 | Single | 13194 | 17071 | 4 | Y | Yes | 16 | 3 | 4 | 80 | 0 | 22 | 2 | 2 | 1 | 0 | 0 | 0 |
| 47 | No | Travel_Frequently | 1379 | Research & Development | 16 | 4 | Medical | 1 | 987 | 3 | Male | 64 | 4 | 2 | Manufacturing Director | 3 | Divorced | 5067 | 6759 | 1 | Y | Yes | 19 | 3 | 3 | 80 | 0 | 20 | 3 | 4 | 19 | 10 | 2 | 7 |
| 36 | No | Non-Travel | 1229 | Sales | 8 | 4 | Technical Degree | 1 | 990 | 1 | Male | 84 | 3 | 2 | Sales Executive | 4 | Divorced | 5079 | 25952 | 4 | Y | No | 13 | 3 | 4 | 80 | 2 | 12 | 3 | 3 | 7 | 7 | 0 | 7 |
| 31 | Yes | Non-Travel | 335 | Research & Development | 9 | 2 | Medical | 1 | 991 | 3 | Male | 46 | 2 | 1 | Research Scientist | 1 | Single | 2321 | 10322 | 0 | Y | Yes | 22 | 4 | 1 | 80 | 0 | 4 | 0 | 3 | 3 | 2 | 1 | 2 |
| 33 | No | Non-Travel | 722 | Sales | 17 | 3 | Life Sciences | 1 | 992 | 4 | Male | 38 | 3 | 4 | Manager | 3 | Single | 17444 | 20489 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 10 | 8 | 6 | 0 |
| 29 | Yes | Travel_Rarely | 906 | Research & Development | 10 | 3 | Life Sciences | 1 | 994 | 4 | Female | 92 | 2 | 1 | Research Scientist | 1 | Single | 2404 | 11479 | 6 | Y | Yes | 20 | 4 | 3 | 80 | 0 | 3 | 5 | 3 | 0 | 0 | 0 | 0 |
| 33 | No | Travel_Rarely | 461 | Research & Development | 13 | 1 | Life Sciences | 1 | 995 | 2 | Female | 53 | 3 | 1 | Research Scientist | 4 | Single | 3452 | 17241 | 3 | Y | No | 18 | 3 | 1 | 80 | 0 | 5 | 4 | 3 | 3 | 2 | 0 | 2 |
| 45 | No | Travel_Rarely | 974 | Research & Development | 1 | 4 | Medical | 1 | 996 | 4 | Female | 91 | 3 | 1 | Laboratory Technician | 4 | Divorced | 2270 | 11005 | 3 | Y | No | 14 | 3 | 4 | 80 | 2 | 8 | 2 | 3 | 5 | 3 | 0 | 2 |
| 50 | No | Travel_Rarely | 1126 | Research & Development | 1 | 2 | Medical | 1 | 997 | 4 | Male | 66 | 3 | 4 | Research Director | 4 | Divorced | 17399 | 6615 | 9 | Y | No | 22 | 4 | 3 | 80 | 1 | 32 | 1 | 2 | 5 | 4 | 1 | 3 |
| 33 | No | Travel_Frequently | 827 | Research & Development | 1 | 4 | Other | 1 | 998 | 3 | Female | 84 | 4 | 2 | Healthcare Representative | 2 | Married | 5488 | 20161 | 1 | Y | Yes | 13 | 3 | 1 | 80 | 1 | 6 | 2 | 3 | 6 | 5 | 1 | 2 |
| 41 | No | Travel_Frequently | 840 | Research & Development | 9 | 3 | Medical | 1 | 999 | 1 | Male | 64 | 3 | 5 | Research Director | 3 | Divorced | 19419 | 3735 | 2 | Y | No | 17 | 3 | 2 | 80 | 1 | 21 | 2 | 4 | 18 | 16 | 0 | 11 |
| 27 | No | Travel_Rarely | 1134 | Research & Development | 16 | 4 | Technical Degree | 1 | 1001 | 3 | Female | 37 | 3 | 1 | Laboratory Technician | 2 | Married | 2811 | 12086 | 9 | Y | No | 14 | 3 | 2 | 80 | 1 | 4 | 2 | 3 | 2 | 2 | 2 | 2 |
| 45 | No | Non-Travel | 248 | Research & Development | 23 | 2 | Life Sciences | 1 | 1002 | 4 | Male | 42 | 3 | 2 | Laboratory Technician | 1 | Married | 3633 | 14039 | 1 | Y | Yes | 15 | 3 | 3 | 80 | 1 | 9 | 2 | 3 | 9 | 8 | 0 | 8 |
| 47 | No | Travel_Rarely | 955 | Sales | 4 | 2 | Life Sciences | 1 | 1003 | 4 | Female | 83 | 3 | 2 | Sales Executive | 4 | Single | 4163 | 8571 | 1 | Y | Yes | 17 | 3 | 3 | 80 | 0 | 9 | 0 | 3 | 9 | 0 | 0 | 7 |
| 30 | Yes | Travel_Rarely | 138 | Research & Development | 22 | 3 | Life Sciences | 1 | 1004 | 1 | Female | 48 | 3 | 1 | Research Scientist | 3 | Married | 2132 | 11539 | 4 | Y | Yes | 11 | 3 | 2 | 80 | 0 | 7 | 2 | 3 | 5 | 2 | 0 | 1 |
| 50 | NA | Travel_Rarely | 939 | Research & Development | 24 | 3 | Life Sciences | 1 | 1005 | 4 | Male | 95 | 3 | 4 | Manufacturing Director | 3 | Married | 13973 | 4161 | 3 | Y | Yes | 18 | 3 | 4 | 80 | 1 | 22 | 2 | 3 | 12 | 11 | 1 | 5 |
| 38 | No | Travel_Frequently | 1391 | Research & Development | 10 | 1 | Medical | 1 | 1006 | 3 | Male | 66 | 3 | 1 | Research Scientist | 3 | Married | 2684 | 12127 | 0 | Y | No | 17 | 3 | 2 | 80 | 1 | 3 | 0 | 2 | 2 | 1 | 0 | 2 |
| 46 | No | Travel_Rarely | 566 | Research & Development | 7 | 2 | Medical | 1 | 1007 | 4 | Male | 75 | 3 | 3 | Manufacturing Director | 3 | Divorced | NA | 24208 | 6 | Y | No | 13 | 3 | 2 | 80 | 1 | 13 | 3 | 3 | 8 | 7 | 0 | 7 |
| 24 | No | Travel_Rarely | 1206 | Research & Development | 17 | 1 | Medical | 1 | 1009 | 4 | Female | 41 | 2 | 2 | Manufacturing Director | 3 | Divorced | 4377 | 24117 | 1 | Y | No | 15 | 3 | 2 | 80 | 2 | 5 | 6 | 3 | 4 | 2 | 3 | 2 |
| 35 | Yes | Travel_Rarely | 622 | Research & Development | 14 | 4 | Other | 1 | 1010 | 3 | Male | 39 | 2 | 1 | Laboratory Technician | 2 | Divorced | 3743 | 10074 | 1 | Y | Yes | 24 | 4 | 4 | 80 | 1 | 5 | 2 | 1 | 4 | 2 | 0 | 2 |
| 31 | No | Travel_Frequently | 853 | Research & Development | 1 | 1 | Life Sciences | 1 | 1011 | 3 | Female | 96 | 3 | 2 | Manufacturing Director | 1 | Married | 4148 | 11275 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 4 | 1 | 3 | 4 | 3 | 0 | 3 |
| 18 | No | Non-Travel | 287 | Research & Development | 5 | 2 | Life Sciences | 1 | 1012 | 2 | Male | 73 | 3 | 1 | Research Scientist | 4 | Single | 1051 | 13493 | 1 | Y | No | 15 | 3 | 4 | 80 | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 0 |
| 54 | No | Travel_Rarely | 1441 | Research & Development | 17 | 3 | Technical Degree | 1 | 1013 | 3 | Female | 56 | 3 | 3 | Manufacturing Director | 3 | Married | 10739 | 13943 | 8 | Y | No | 11 | 3 | 3 | 80 | 1 | 22 | 2 | 3 | 10 | 7 | 0 | 8 |
| 35 | No | Travel_Rarely | 583 | Research & Development | 25 | 4 | Medical | 1 | 1014 | 3 | Female | 57 | 3 | 3 | Healthcare Representative | 3 | Divorced | 10388 | 6975 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 16 | 3 | 2 | 16 | 10 | 10 | 1 |
| 30 | No | Travel_Rarely | 153 | Research & Development | 8 | 2 | Life Sciences | 1 | 1015 | 2 | Female | 73 | 4 | 3 | Research Director | 1 | Married | 11416 | 17802 | 0 | Y | Yes | 12 | 3 | 3 | 80 | 3 | 9 | 4 | 2 | 8 | 7 | 1 | 7 |
| 20 | Yes | Travel_Rarely | 1097 | Research & Development | 11 | 3 | Medical | 1 | 1016 | 4 | Female | 98 | 2 | 1 | Research Scientist | 1 | Single | 2600 | 18275 | 1 | Y | Yes | 15 | 3 | 1 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 30 | Yes | Travel_Frequently | 109 | Research & Development | 5 | 3 | Medical | 1 | 1017 | 2 | Female | 60 | 3 | 1 | Laboratory Technician | 2 | Single | 2422 | 25725 | 0 | Y | No | 17 | 3 | 1 | 80 | 0 | 4 | 3 | 3 | 3 | 2 | 1 | 2 |
| 26 | No | Travel_Rarely | 1066 | Research & Development | 2 | 2 | Medical | 1 | 1018 | 4 | Male | 32 | 4 | 2 | Manufacturing Director | 4 | Married | 5472 | 3334 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 8 | 2 | 3 | 8 | 7 | 1 | 3 |
| 22 | No | Travel_Rarely | 217 | Research & Development | 8 | 1 | Life Sciences | 1 | 1019 | 2 | Male | 94 | 1 | 1 | Laboratory Technician | 1 | Married | 2451 | 6881 | 1 | Y | No | 15 | 3 | 1 | 80 | 1 | 4 | 3 | 2 | 4 | 3 | 1 | 1 |
| 48 | No | Travel_Rarely | 277 | Research & Development | 6 | 3 | Life Sciences | 1 | 1022 | 1 | Male | 97 | 2 | 2 | Healthcare Representative | 3 | Single | NA | 13119 | 2 | Y | No | 13 | 3 | 4 | 80 | 0 | 19 | 0 | 3 | 2 | 2 | 2 | 2 |
| 48 | No | Travel_Rarely | 1355 | Research & Development | 4 | 4 | Life Sciences | 1 | 1024 | 3 | Male | 78 | 2 | 3 | Healthcare Representative | 3 | Single | 10999 | 22245 | 7 | Y | No | 14 | 3 | 2 | 80 | 0 | 27 | 3 | 3 | 15 | 11 | 4 | 8 |
| 41 | No | Travel_Rarely | 549 | Research & Development | 7 | 2 | Medical | 1 | 1025 | 4 | Female | 42 | 3 | 2 | Manufacturing Director | 3 | Single | 5003 | 23371 | 6 | Y | No | 14 | 3 | 2 | 80 | 0 | 8 | 6 | 3 | 2 | 2 | 2 | 1 |
| 39 | NA | Travel_Rarely | 466 | Research & Development | 1 | 1 | Life Sciences | 1 | 1026 | 4 | Female | 65 | 2 | 4 | Manufacturing Director | 4 | Married | 12742 | 7060 | 1 | Y | No | 16 | 3 | 3 | 80 | 1 | 21 | 3 | 3 | 21 | 6 | 11 | 8 |
| 27 | No | Travel_Rarely | 1055 | Research & Development | 2 | 4 | Life Sciences | 1 | 1027 | 1 | Female | 47 | 3 | 2 | Manufacturing Director | 4 | Married | 4227 | 4658 | 0 | Y | No | 18 | 3 | 2 | 80 | 1 | 4 | 2 | 3 | 3 | 2 | 2 | 2 |
| 35 | No | Travel_Rarely | 802 | Research & Development | 10 | 3 | Other | 1 | 1028 | 2 | Male | 45 | 3 | 1 | Laboratory Technician | 4 | Divorced | 3917 | 9541 | 1 | Y | No | 20 | 4 | 1 | 80 | 1 | 3 | 4 | 2 | 3 | 2 | 1 | 2 |
| 42 | No | Travel_Rarely | 265 | Sales | 5 | 2 | Marketing | 1 | 1029 | 4 | Male | 90 | 3 | 5 | Manager | 3 | Married | 18303 | 7770 | 6 | Y | No | 13 | 3 | 2 | 80 | 0 | 21 | 3 | 4 | 1 | 0 | 0 | 0 |
| 50 | No | Travel_Rarely | 804 | Research & Development | 9 | 3 | Life Sciences | 1 | 1030 | 1 | Male | 64 | 3 | 1 | Laboratory Technician | 4 | Married | 2380 | 20165 | 4 | Y | No | 18 | 3 | 2 | 80 | 0 | 8 | 5 | 3 | 1 | 0 | 0 | 0 |
| 59 | No | Travel_Rarely | 715 | Research & Development | 2 | 3 | Life Sciences | 1 | 1032 | 3 | Female | 69 | 2 | 4 | Manufacturing Director | 4 | Single | 13726 | 21829 | 3 | Y | Yes | 13 | 3 | 1 | 80 | 0 | 30 | 4 | 3 | 5 | 3 | 4 | 3 |
| 37 | NA | Travel_Rarely | 1141 | Research & Development | 11 | 2 | Medical | 1 | 1033 | 1 | Female | 61 | 1 | 2 | Healthcare Representative | 2 | Married | 4777 | 14382 | 5 | Y | No | 15 | 3 | 1 | 80 | 0 | 15 | 2 | 1 | 1 | 0 | 0 | 0 |
| 55 | No | Travel_Frequently | 135 | Research & Development | 18 | 4 | Medical | 1 | 1034 | 3 | Male | 62 | 3 | 2 | Healthcare Representative | 2 | Married | 6385 | 12992 | 3 | Y | Yes | 14 | 3 | 4 | 80 | 2 | 17 | 3 | 3 | 8 | 7 | 6 | 7 |
| 41 | No | Non-Travel | 247 | Research & Development | 7 | 1 | Life Sciences | 1 | 1035 | 2 | Female | 55 | 1 | 5 | Research Director | 3 | Divorced | 19973 | 20284 | 1 | Y | No | 22 | 4 | 2 | 80 | 2 | 21 | 3 | 3 | 21 | 16 | 5 | 10 |
| 38 | No | Travel_Rarely | 1035 | Sales | 3 | 4 | Life Sciences | 1 | 1036 | 2 | Male | 42 | 3 | 2 | Sales Executive | 4 | Single | NA | 4981 | 8 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 19 | 1 | 3 | 1 | 0 | 0 | 0 |
| 26 | Yes | Non-Travel | 265 | Sales | 29 | 2 | Medical | 1 | 1037 | 2 | Male | 79 | 1 | 2 | Sales Executive | 1 | Single | NA | 21813 | 8 | Y | No | 18 | 3 | 4 | 80 | 0 | 7 | 6 | 3 | 2 | 2 | 2 | 2 |
| 52 | Yes | Travel_Rarely | 266 | Sales | 2 | 1 | Marketing | 1 | 1038 | 1 | Female | 57 | 1 | 5 | Manager | 4 | Married | 19845 | 25846 | 1 | Y | No | 15 | 3 | 4 | 80 | 1 | 33 | 3 | 3 | 32 | 14 | 6 | 9 |
| 44 | No | Travel_Rarely | 1448 | Sales | 28 | 3 | Medical | 1 | 1039 | 4 | Female | 53 | 4 | 4 | Sales Executive | 4 | Married | 13320 | 11737 | 3 | Y | Yes | 18 | 3 | 3 | 80 | 1 | 23 | 2 | 3 | 12 | 11 | 11 | 11 |
| 50 | No | Non-Travel | 145 | Sales | 1 | 3 | Life Sciences | 1 | 1040 | 4 | Female | 95 | 3 | 2 | Sales Executive | 3 | Married | 6347 | 24920 | 0 | Y | No | 12 | 3 | 1 | 80 | 1 | 19 | 3 | 3 | 18 | 7 | 0 | 13 |
| 36 | Yes | Travel_Rarely | 885 | Research & Development | 16 | 4 | Life Sciences | 1 | 1042 | 3 | Female | 43 | 4 | 1 | Laboratory Technician | 1 | Single | 2743 | 8269 | 1 | Y | No | 16 | 3 | 3 | 80 | 0 | 18 | 1 | 3 | 17 | 13 | 15 | 14 |
| 39 | NA | Travel_Frequently | 945 | Research & Development | 22 | 3 | Medical | 1 | 1043 | 4 | Female | 82 | 3 | 3 | Manufacturing Director | 1 | Single | 10880 | 5083 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 21 | 2 | 3 | 21 | 6 | 2 | 8 |
| 33 | No | Non-Travel | 1038 | Sales | 8 | 1 | Life Sciences | 1 | 1044 | 2 | Female | 88 | 2 | 1 | Sales Representative | 4 | Single | NA | 21437 | 0 | Y | No | 19 | 3 | 4 | 80 | 0 | 3 | 2 | 2 | 2 | 2 | 2 | 2 |
| 45 | No | Travel_Rarely | 1234 | Sales | 11 | 2 | Life Sciences | 1 | 1045 | 4 | Female | 90 | 3 | 4 | Manager | 4 | Married | 17650 | 5404 | 3 | Y | No | 13 | 3 | 2 | 80 | 1 | 26 | 4 | 4 | 9 | 3 | 1 | 1 |
| 32 | NA | Non-Travel | 1109 | Research & Development | 29 | 4 | Medical | 1 | 1046 | 4 | Female | 69 | 3 | 1 | Laboratory Technician | 3 | Single | 4025 | 11135 | 9 | Y | No | 12 | 3 | 2 | 80 | 0 | 10 | 2 | 3 | 8 | 7 | 7 | 7 |
| 34 | No | Travel_Rarely | 216 | Sales | 1 | 4 | Marketing | 1 | 1047 | 2 | Male | 75 | 4 | 2 | Sales Executive | 4 | Divorced | 9725 | 12278 | 0 | Y | No | 11 | 3 | 4 | 80 | 1 | 16 | 2 | 2 | 15 | 1 | 0 | 9 |
| 59 | No | Travel_Rarely | 1089 | Sales | 1 | 2 | Technical Degree | 1 | 1048 | 2 | Male | 66 | 3 | 3 | Manager | 4 | Married | 11904 | 11038 | 3 | Y | Yes | 14 | 3 | 3 | 80 | 1 | 14 | 1 | 1 | 6 | 4 | 0 | 4 |
| 45 | No | Travel_Rarely | 788 | Human Resources | 24 | 4 | Medical | 1 | 1049 | 2 | Male | 36 | 3 | 1 | Human Resources | 2 | Single | 2177 | 8318 | 1 | Y | No | 16 | 3 | 1 | 80 | 0 | 6 | 3 | 3 | 6 | 3 | 0 | 4 |
| 53 | No | Travel_Frequently | 124 | Sales | 2 | 3 | Marketing | 1 | 1050 | 3 | Female | 38 | 2 | 3 | Sales Executive | 2 | Married | 7525 | 23537 | 2 | Y | No | 12 | 3 | 1 | 80 | 1 | 30 | 2 | 3 | 15 | 7 | 6 | 12 |
| 36 | Yes | Travel_Rarely | 660 | Research & Development | 15 | 3 | Other | 1 | 1052 | 1 | Male | 81 | 3 | 2 | Laboratory Technician | 3 | Divorced | 4834 | 7858 | 7 | Y | No | 14 | 3 | 2 | 80 | 1 | 9 | 3 | 2 | 1 | 0 | 0 | 0 |
| 26 | NA | Travel_Frequently | 342 | Research & Development | 2 | 3 | Life Sciences | 1 | 1053 | 1 | Male | 57 | 3 | 1 | Research Scientist | 1 | Married | NA | 15346 | 6 | Y | Yes | 14 | 3 | 2 | 80 | 1 | 6 | 2 | 3 | 3 | 2 | 1 | 2 |
| 34 | No | Travel_Rarely | 1333 | Sales | 10 | 4 | Life Sciences | 1 | 1055 | 3 | Female | 87 | 3 | 1 | Sales Representative | 3 | Married | 2220 | 18410 | 1 | Y | Yes | 19 | 3 | 4 | 80 | 1 | 1 | 2 | 3 | 1 | 1 | 0 | 0 |
| 28 | No | Travel_Rarely | 1144 | Sales | 10 | 1 | Medical | 1 | 1056 | 4 | Male | 74 | 3 | 1 | Sales Representative | 2 | Married | NA | 23384 | 1 | Y | No | 22 | 4 | 2 | 80 | 0 | 1 | 5 | 3 | 1 | 0 | 0 | 0 |
| 38 | NA | Travel_Frequently | 1186 | Research & Development | 3 | 4 | Other | 1 | 1060 | 3 | Male | 44 | 3 | 1 | Research Scientist | 3 | Married | 2821 | 2997 | 3 | Y | No | 16 | 3 | 1 | 80 | 1 | 8 | 2 | 3 | 2 | 2 | 2 | 2 |
| 50 | No | Travel_Rarely | 1464 | Research & Development | 2 | 4 | Medical | 1 | 1061 | 2 | Male | 62 | 3 | 5 | Research Director | 3 | Married | 19237 | 12853 | 2 | Y | Yes | 11 | 3 | 4 | 80 | 1 | 29 | 2 | 2 | 8 | 1 | 7 | 7 |
| 37 | No | Travel_Rarely | 124 | Research & Development | 3 | 3 | Other | 1 | 1062 | 4 | Female | 35 | 3 | 2 | Healthcare Representative | 2 | Single | NA | 13848 | 3 | Y | No | 15 | 3 | 1 | 80 | 0 | 8 | 3 | 2 | 4 | 3 | 0 | 1 |
| 40 | No | Travel_Rarely | 300 | Sales | 26 | 3 | Marketing | 1 | 1066 | 3 | Male | 74 | 3 | 2 | Sales Executive | 1 | Married | 8396 | 22217 | 1 | Y | No | 14 | 3 | 2 | 80 | 1 | 8 | 3 | 2 | 7 | 7 | 7 | 5 |
| 26 | No | Travel_Frequently | 921 | Research & Development | 1 | 1 | Medical | 1 | 1068 | 1 | Female | 66 | 2 | 1 | Research Scientist | 3 | Divorced | 2007 | 25265 | 1 | Y | No | 13 | 3 | 3 | 80 | 2 | 5 | 5 | 3 | 5 | 3 | 1 | 3 |
| 46 | No | Travel_Rarely | 430 | Research & Development | 1 | 4 | Medical | 1 | 1069 | 4 | Male | 40 | 3 | 5 | Research Director | 4 | Divorced | 19627 | 21445 | 9 | Y | No | 17 | 3 | 4 | 80 | 2 | 23 | 0 | 3 | 2 | 2 | 2 | 2 |
| 54 | No | Travel_Rarely | 1082 | Sales | 2 | 4 | Life Sciences | 1 | 1070 | 3 | Female | 41 | 2 | 3 | Sales Executive | 3 | Married | 10686 | 8392 | 6 | Y | No | 11 | 3 | 2 | 80 | 1 | 13 | 4 | 3 | 9 | 4 | 7 | 0 |
| 56 | No | Travel_Frequently | 1240 | Research & Development | 9 | 3 | Medical | 1 | 1071 | 1 | Female | 63 | 3 | 1 | Research Scientist | 3 | Married | 2942 | 12154 | 2 | Y | No | 19 | 3 | 2 | 80 | 1 | 18 | 4 | 3 | 5 | 4 | 0 | 3 |
| NA | No | Travel_Rarely | 796 | Research & Development | 12 | 5 | Medical | 1 | 1073 | 4 | Female | 51 | 2 | 3 | Manufacturing Director | 4 | Single | 8858 | 15669 | 0 | Y | No | 11 | 3 | 2 | 80 | 0 | 15 | 2 | 2 | 14 | 8 | 7 | 8 |
| 55 | NA | Non-Travel | 444 | Research & Development | 2 | 1 | Medical | 1 | 1074 | 3 | Male | 40 | 2 | 4 | Manager | 1 | Single | 16756 | 17323 | 7 | Y | No | 15 | 3 | 2 | 80 | 0 | 31 | 3 | 4 | 9 | 7 | 6 | 2 |
| 43 | No | Travel_Rarely | 415 | Sales | 25 | 3 | Medical | 1 | 1076 | 3 | Male | 79 | 2 | 3 | Sales Executive | 4 | Divorced | 10798 | 5268 | 5 | Y | No | 13 | 3 | 3 | 80 | 1 | 18 | 5 | 3 | 1 | 0 | 0 | 0 |
| 20 | Yes | Travel_Frequently | 769 | Sales | 9 | 3 | Marketing | 1 | 1077 | 4 | Female | 54 | 3 | 1 | Sales Representative | 4 | Single | 2323 | 17205 | 1 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 0 | 2 |
| 21 | Yes | Travel_Rarely | 1334 | Research & Development | 10 | 3 | Life Sciences | 1 | 1079 | 3 | Female | 36 | 2 | 1 | Laboratory Technician | 1 | Single | 1416 | 17258 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 1 | 6 | 2 | 1 | 0 | 1 | 0 |
| 46 | No | Travel_Rarely | 1003 | Research & Development | 8 | 4 | Life Sciences | 1 | 1080 | 4 | Female | 74 | 2 | 2 | Research Scientist | 1 | Divorced | 4615 | 21029 | 8 | Y | Yes | 23 | 4 | 1 | 80 | 3 | 19 | 2 | 3 | 16 | 13 | 1 | 7 |
| 51 | Yes | Travel_Rarely | 1323 | Research & Development | 4 | 4 | Life Sciences | 1 | 1081 | 1 | Male | 34 | 3 | 1 | Research Scientist | 3 | Married | 2461 | 10332 | 9 | Y | Yes | 12 | 3 | 3 | 80 | 3 | 18 | 2 | 4 | 10 | 0 | 2 | 7 |
| 28 | Yes | Non-Travel | 1366 | Research & Development | 24 | 2 | Technical Degree | 1 | 1082 | 2 | Male | 72 | 2 | 3 | Healthcare Representative | 1 | Single | 8722 | 12355 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 10 | 2 | 2 | 10 | 7 | 1 | 9 |
| 26 | No | Travel_Rarely | 192 | Research & Development | 1 | 2 | Medical | 1 | 1083 | 1 | Male | 59 | 2 | 1 | Laboratory Technician | 1 | Married | 3955 | 11141 | 1 | Y | No | 16 | 3 | 1 | 80 | 2 | 6 | 2 | 3 | 5 | 3 | 1 | 3 |
| 30 | No | Travel_Rarely | 1176 | Research & Development | 20 | 3 | Other | 1 | 1084 | 3 | Male | 85 | 3 | 2 | Manufacturing Director | 1 | Married | NA | 9096 | 0 | Y | No | 15 | 3 | 3 | 80 | 1 | 7 | 1 | 2 | 6 | 2 | 0 | 2 |
| 41 | No | Travel_Rarely | 509 | Research & Development | 7 | 2 | Technical Degree | 1 | 1085 | 2 | Female | 43 | 4 | 1 | Research Scientist | 3 | Married | 3376 | 18863 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 10 | 3 | 3 | 10 | 6 | 0 | 8 |
| 38 | No | Travel_Rarely | 330 | Research & Development | 17 | 1 | Life Sciences | 1 | 1088 | 3 | Female | 65 | 2 | 3 | Healthcare Representative | 3 | Married | NA | 24608 | 0 | Y | No | 18 | 3 | 1 | 80 | 1 | 20 | 4 | 2 | 19 | 9 | 1 | 9 |
| NA | No | Travel_Rarely | 1492 | Research & Development | 20 | 4 | Technical Degree | 1 | 1092 | 1 | Male | 61 | 3 | 3 | Healthcare Representative | 4 | Married | 10322 | 26542 | 4 | Y | No | 20 | 4 | 4 | 80 | 1 | 14 | 6 | 3 | 11 | 10 | 11 | 1 |
| 27 | No | Non-Travel | 1277 | Research & Development | 8 | 5 | Life Sciences | 1 | 1094 | 1 | Male | 87 | 1 | 1 | Laboratory Technician | 3 | Married | 4621 | 5869 | 1 | Y | No | 19 | 3 | 4 | 80 | 3 | 3 | 4 | 3 | 3 | 2 | 1 | 2 |
| 55 | No | Travel_Frequently | 1091 | Research & Development | 2 | 1 | Life Sciences | 1 | 1096 | 4 | Male | 65 | 3 | 3 | Manufacturing Director | 2 | Married | 10976 | 15813 | 3 | Y | No | 18 | 3 | 2 | 80 | 1 | 23 | 4 | 3 | 3 | 2 | 1 | 2 |
| 28 | No | Travel_Rarely | 857 | Research & Development | 10 | 3 | Other | 1 | 1097 | 3 | Female | 59 | 3 | 2 | Research Scientist | 3 | Single | 3660 | 7909 | 3 | Y | No | 13 | 3 | 4 | 80 | 0 | 10 | 4 | 4 | 8 | 7 | 1 | 7 |
| 44 | Yes | Travel_Rarely | 1376 | Human Resources | 1 | 2 | Medical | 1 | 1098 | 2 | Male | 91 | 2 | 3 | Human Resources | 1 | Married | 10482 | 2326 | 9 | Y | No | 14 | 3 | 4 | 80 | 1 | 24 | 1 | 3 | 20 | 6 | 3 | 6 |
| 33 | No | Travel_Rarely | 654 | Research & Development | 5 | 3 | Life Sciences | 1 | 1099 | 4 | Male | 34 | 2 | 3 | Healthcare Representative | 4 | Divorced | 7119 | 21214 | 4 | Y | No | 15 | 3 | 3 | 80 | 1 | 9 | 2 | 3 | 3 | 2 | 1 | 2 |
| 35 | Yes | Travel_Rarely | 1204 | Sales | 4 | 3 | Technical Degree | 1 | 1100 | 4 | Male | 86 | 3 | 3 | Sales Executive | 1 | Single | 9582 | 10333 | 0 | Y | Yes | 22 | 4 | 1 | 80 | 0 | 9 | 2 | 3 | 8 | 7 | 4 | 7 |
| 33 | Yes | Travel_Frequently | 827 | Research & Development | 29 | 4 | Medical | 1 | 1101 | 1 | Female | 54 | 2 | 2 | Research Scientist | 3 | Single | 4508 | 3129 | 1 | Y | No | 22 | 4 | 2 | 80 | 0 | 14 | 4 | 3 | 13 | 7 | 3 | 8 |
| 28 | No | Travel_Rarely | 895 | Research & Development | 15 | 2 | Life Sciences | 1 | 1102 | 1 | Male | 50 | 3 | 1 | Laboratory Technician | 3 | Divorced | 2207 | 22482 | 1 | Y | No | 16 | 3 | 4 | 80 | 1 | 4 | 5 | 2 | 4 | 2 | 2 | 2 |
| NA | No | Travel_Frequently | 618 | Research & Development | 3 | 1 | Life Sciences | 1 | 1103 | 1 | Male | 45 | 3 | 2 | Healthcare Representative | 4 | Single | 7756 | 22266 | 0 | Y | No | 17 | 3 | 3 | 80 | 0 | 7 | 1 | 2 | 6 | 2 | 0 | 4 |
| 37 | NA | Travel_Rarely | 309 | Sales | 10 | 4 | Life Sciences | 1 | 1105 | 4 | Female | 88 | 2 | 2 | Sales Executive | 4 | Divorced | 6694 | 24223 | 2 | Y | Yes | 14 | 3 | 3 | 80 | 3 | 8 | 5 | 3 | 1 | 0 | 0 | 0 |
| 25 | Yes | Travel_Rarely | 1219 | Research & Development | 4 | 1 | Technical Degree | 1 | 1106 | 4 | Male | 32 | 3 | 1 | Laboratory Technician | 4 | Married | 3691 | 4605 | 1 | Y | Yes | 15 | 3 | 2 | 80 | 1 | 7 | 3 | 4 | 7 | 7 | 5 | 6 |
| 26 | Yes | Travel_Rarely | 1330 | Research & Development | 21 | 3 | Medical | 1 | 1107 | 1 | Male | 37 | 3 | 1 | Laboratory Technician | 3 | Divorced | 2377 | 19373 | 1 | Y | No | 20 | 4 | 3 | 80 | 1 | 1 | 0 | 2 | 1 | 1 | 0 | 0 |
| 33 | Yes | Travel_Rarely | 1017 | Research & Development | 25 | 3 | Medical | 1 | 1108 | 1 | Male | 55 | 2 | 1 | Research Scientist | 2 | Single | 2313 | 2993 | 4 | Y | Yes | 20 | 4 | 2 | 80 | 0 | 5 | 0 | 3 | 2 | 2 | 2 | 2 |
| 42 | No | Travel_Rarely | 469 | Research & Development | 2 | 2 | Medical | 1 | 1109 | 4 | Male | 35 | 3 | 4 | Manager | 1 | Married | 17665 | 14399 | 0 | Y | No | 17 | 3 | 4 | 80 | 1 | 23 | 3 | 3 | 22 | 6 | 13 | 7 |
| 28 | Yes | Travel_Frequently | 1009 | Research & Development | 1 | 3 | Medical | 1 | 1111 | 1 | Male | 45 | 2 | 1 | Laboratory Technician | 2 | Divorced | 2596 | 7160 | 1 | Y | No | 15 | 3 | 1 | 80 | 2 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 50 | Yes | Travel_Frequently | 959 | Sales | 1 | 4 | Other | 1 | 1113 | 4 | Male | 81 | 3 | 2 | Sales Executive | 3 | Single | 4728 | 17251 | 3 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 5 | 4 | 3 | 0 | 0 | 0 | 0 |
| 33 | No | Travel_Frequently | 970 | Sales | 7 | 3 | Life Sciences | 1 | 1114 | 4 | Female | 30 | 3 | 2 | Sales Executive | 2 | Married | 4302 | 13401 | 0 | Y | No | 17 | 3 | 3 | 80 | 1 | 4 | 3 | 3 | 3 | 2 | 0 | 2 |
| 34 | No | Non-Travel | 697 | Research & Development | 3 | 4 | Life Sciences | 1 | 1115 | 3 | Male | 40 | 2 | 1 | Research Scientist | 4 | Married | 2979 | 22478 | 3 | Y | No | 17 | 3 | 4 | 80 | 3 | 6 | 2 | 3 | 0 | 0 | 0 | 0 |
| 48 | No | Non-Travel | 1262 | Research & Development | 1 | 4 | Medical | 1 | 1116 | 1 | Male | 35 | 4 | 4 | Manager | 4 | Single | 16885 | 16154 | 2 | Y | No | 22 | 4 | 3 | 80 | 0 | 27 | 3 | 2 | 5 | 4 | 2 | 1 |
| 45 | No | Non-Travel | 1050 | Sales | 9 | 4 | Life Sciences | 1 | 1117 | 2 | Female | 65 | 2 | 2 | Sales Executive | 3 | Married | 5593 | 17970 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 15 | 2 | 3 | 15 | 10 | 4 | 12 |
| NA | No | Travel_Rarely | 994 | Research & Development | 7 | 4 | Life Sciences | 1 | 1118 | 2 | Male | 87 | 3 | 3 | Healthcare Representative | 2 | Single | 10445 | 15322 | 7 | Y | No | 19 | 3 | 4 | 80 | 0 | 18 | 4 | 3 | 8 | 6 | 4 | 0 |
| 38 | No | Travel_Rarely | 770 | Sales | 10 | 4 | Marketing | 1 | 1119 | 3 | Male | 73 | 2 | 3 | Sales Executive | 3 | Divorced | 8740 | 5569 | 0 | Y | Yes | 14 | 3 | 2 | 80 | 2 | 9 | 2 | 3 | 8 | 7 | 2 | 7 |
| 29 | No | Travel_Rarely | 1107 | Research & Development | 28 | 4 | Life Sciences | 1 | 1120 | 3 | Female | 93 | 3 | 1 | Research Scientist | 4 | Divorced | 2514 | 26968 | 4 | Y | No | 22 | 4 | 1 | 80 | 1 | 11 | 1 | 3 | 7 | 5 | 1 | 7 |
| 28 | No | Travel_Rarely | 950 | Research & Development | 3 | 3 | Medical | 1 | 1121 | 4 | Female | 93 | 3 | 3 | Manufacturing Director | 2 | Divorced | 7655 | 8039 | 0 | Y | No | 17 | 3 | 2 | 80 | 3 | 10 | 3 | 2 | 9 | 7 | 1 | 7 |
| 46 | No | Travel_Rarely | 406 | Sales | 3 | 1 | Marketing | 1 | 1124 | 1 | Male | 52 | 3 | 4 | Manager | 3 | Married | 17465 | 15596 | 3 | Y | No | 12 | 3 | 4 | 80 | 1 | 23 | 3 | 3 | 12 | 9 | 4 | 9 |
| 38 | No | Travel_Rarely | 130 | Sales | 2 | 2 | Marketing | 1 | 1125 | 4 | Male | 32 | 3 | 3 | Sales Executive | 2 | Single | NA | 20619 | 7 | Y | No | 16 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 1 | 0 | 0 | 0 |
| 43 | No | Travel_Frequently | 1082 | Research & Development | 27 | 3 | Life Sciences | 1 | 1126 | 3 | Female | 83 | 3 | 3 | Manufacturing Director | 1 | Married | 10820 | 11535 | 8 | Y | No | 11 | 3 | 3 | 80 | 1 | 18 | 1 | 3 | 8 | 7 | 0 | 1 |
| 39 | Yes | Travel_Frequently | 203 | Research & Development | 2 | 3 | Life Sciences | 1 | 1127 | 1 | Male | 84 | 3 | 4 | Healthcare Representative | 4 | Divorced | 12169 | 13547 | 7 | Y | No | 11 | 3 | 4 | 80 | 3 | 21 | 4 | 3 | 18 | 7 | 11 | 5 |
| 40 | No | Travel_Rarely | 1308 | Research & Development | 14 | 3 | Medical | 1 | 1128 | 3 | Male | 44 | 2 | 5 | Research Director | 3 | Single | 19626 | 17544 | 1 | Y | No | 14 | 3 | 1 | 80 | 0 | 21 | 2 | 4 | 20 | 7 | 4 | 9 |
| 21 | No | Travel_Rarely | 984 | Research & Development | 1 | 1 | Technical Degree | 1 | 1131 | 4 | Female | 70 | 2 | 1 | Research Scientist | 2 | Single | 2070 | 25326 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 2 | 6 | 4 | 2 | 2 | 2 | 2 |
| 39 | No | Non-Travel | 439 | Research & Development | 9 | 3 | Life Sciences | 1 | 1132 | 3 | Male | 70 | 3 | 2 | Laboratory Technician | 2 | Single | 6782 | 8770 | 9 | Y | No | 15 | 3 | 3 | 80 | 0 | 9 | 2 | 2 | 5 | 4 | 0 | 3 |
| 36 | No | Non-Travel | 217 | Research & Development | 18 | 4 | Life Sciences | 1 | 1133 | 1 | Male | 78 | 3 | 2 | Manufacturing Director | 4 | Single | 7779 | 23238 | 2 | Y | No | 20 | 4 | 1 | 80 | 0 | 18 | 0 | 3 | 11 | 9 | 0 | 9 |
| 31 | No | Travel_Frequently | 793 | Sales | 20 | 3 | Life Sciences | 1 | 1135 | 3 | Male | 67 | 4 | 1 | Sales Representative | 4 | Married | 2791 | 21981 | 0 | Y | No | 12 | 3 | 1 | 80 | 1 | 3 | 4 | 3 | 2 | 2 | 2 | 2 |
| 28 | No | Travel_Rarely | 1451 | Research & Development | 2 | 1 | Life Sciences | 1 | 1136 | 1 | Male | 67 | 2 | 1 | Research Scientist | 2 | Married | 3201 | 19911 | 0 | Y | No | 17 | 3 | 1 | 80 | 0 | 6 | 2 | 1 | 5 | 3 | 0 | 4 |
| 35 | No | Travel_Frequently | 1182 | Sales | 11 | 2 | Marketing | 1 | 1137 | 4 | Male | 54 | 3 | 2 | Sales Executive | 4 | Divorced | 4968 | 18500 | 1 | Y | No | 11 | 3 | 4 | 80 | 1 | 5 | 3 | 3 | 5 | 2 | 0 | 2 |
| 49 | No | Travel_Rarely | 174 | Sales | 8 | 4 | Technical Degree | 1 | 1138 | 4 | Male | 56 | 2 | 4 | Sales Executive | 2 | Married | 13120 | 11879 | 6 | Y | No | 17 | 3 | 2 | 80 | 1 | 22 | 3 | 3 | 9 | 8 | 2 | 3 |
| 34 | No | Travel_Frequently | 1003 | Research & Development | 2 | 2 | Life Sciences | 1 | 1140 | 4 | Male | 95 | 3 | 2 | Manufacturing Director | 3 | Single | 4033 | 15834 | 2 | Y | No | 11 | 3 | 4 | 80 | 0 | 5 | 3 | 2 | 3 | 2 | 0 | 2 |
| 29 | No | Travel_Frequently | 490 | Research & Development | 10 | 3 | Life Sciences | 1 | 1143 | 4 | Female | 61 | 3 | 1 | Research Scientist | 2 | Divorced | 3291 | 17940 | 0 | Y | No | 14 | 3 | 4 | 80 | 2 | 8 | 2 | 2 | 7 | 5 | 1 | 1 |
| 42 | No | Travel_Rarely | 188 | Research & Development | 29 | 3 | Medical | 1 | 1148 | 2 | Male | 56 | 1 | 2 | Laboratory Technician | 4 | Single | 4272 | 9558 | 4 | Y | No | 19 | 3 | 1 | 80 | 0 | 16 | 3 | 3 | 1 | 0 | 0 | 0 |
| 29 | No | Travel_Rarely | 718 | Research & Development | 8 | 1 | Medical | 1 | 1150 | 2 | Male | 79 | 2 | 2 | Manufacturing Director | 4 | Married | NA | 17689 | 1 | Y | Yes | 15 | 3 | 3 | 80 | 1 | 10 | 2 | 2 | 10 | 7 | 1 | 2 |
| 38 | No | Travel_Rarely | 433 | Human Resources | 1 | 3 | Human Resources | 1 | 1152 | 3 | Male | 37 | 4 | 1 | Human Resources | 3 | Married | 2844 | 6004 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 7 | 2 | 4 | 7 | 6 | 5 | 0 |
| 28 | No | Travel_Frequently | 773 | Research & Development | 6 | 3 | Life Sciences | 1 | 1154 | 3 | Male | 39 | 2 | 1 | Research Scientist | 3 | Divorced | 2703 | 22088 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 1 | 3 | 2 | 3 | 3 | 1 | 0 | 2 |
| 18 | Yes | Non-Travel | 247 | Research & Development | 8 | 1 | Medical | 1 | 1156 | 3 | Male | 80 | 3 | 1 | Laboratory Technician | 3 | Single | 1904 | 13556 | 1 | Y | No | 12 | 3 | 4 | 80 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 |
| 33 | NA | Travel_Rarely | 603 | Sales | 9 | 4 | Marketing | 1 | 1157 | 1 | Female | 77 | 3 | 2 | Sales Executive | 1 | Single | 8224 | 18385 | 0 | Y | Yes | 17 | 3 | 1 | 80 | 0 | 6 | 3 | 3 | 5 | 2 | 0 | 3 |
| 41 | No | Travel_Rarely | 167 | Research & Development | 12 | 4 | Life Sciences | 1 | 1158 | 2 | Male | 46 | 3 | 1 | Laboratory Technician | 4 | Married | NA | 9051 | 3 | Y | Yes | 11 | 3 | 1 | 80 | 1 | 6 | 4 | 3 | 1 | 0 | 0 | 0 |
| 31 | Yes | Travel_Frequently | 874 | Research & Development | 15 | 3 | Medical | 1 | 1160 | 3 | Male | 72 | 3 | 1 | Laboratory Technician | 3 | Married | 2610 | 6233 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 2 | 5 | 2 | 2 | 2 | 2 | 2 |
| 37 | No | Travel_Rarely | 367 | Research & Development | 25 | 2 | Medical | 1 | 1161 | 3 | Female | 52 | 2 | 2 | Healthcare Representative | 4 | Divorced | 5731 | 17171 | 7 | Y | No | 13 | 3 | 3 | 80 | 2 | 9 | 2 | 3 | 6 | 2 | 1 | 3 |
| 27 | No | Travel_Rarely | 199 | Research & Development | 6 | 3 | Life Sciences | 1 | 1162 | 4 | Male | 55 | 2 | 1 | Research Scientist | 3 | Married | 2539 | 7950 | 1 | Y | No | 13 | 3 | 3 | 80 | 1 | 4 | 0 | 3 | 4 | 2 | 2 | 2 |
| NA | No | Travel_Rarely | 1400 | Sales | 9 | 1 | Life Sciences | 1 | 1163 | 2 | Female | 70 | 3 | 2 | Sales Executive | 3 | Married | 5714 | 5829 | 1 | Y | No | 20 | 4 | 1 | 80 | 0 | 6 | 3 | 2 | 6 | 5 | 1 | 3 |
| 35 | No | Travel_Rarely | 528 | Human Resources | 8 | 4 | Technical Degree | 1 | 1164 | 3 | Male | 100 | 3 | 1 | Human Resources | 3 | Single | 4323 | 7108 | 1 | Y | No | 17 | 3 | 2 | 80 | 0 | 6 | 2 | 1 | 5 | 4 | 1 | 4 |
| 29 | Yes | Travel_Rarely | 408 | Sales | 23 | 1 | Life Sciences | 1 | 1165 | 4 | Female | 45 | 2 | 3 | Sales Executive | 1 | Married | 7336 | 11162 | 1 | Y | No | 13 | 3 | 1 | 80 | 1 | 11 | 3 | 1 | 11 | 8 | 3 | 10 |
| 40 | No | Travel_Frequently | 593 | Research & Development | 9 | 4 | Medical | 1 | 1166 | 2 | Female | 88 | 3 | 3 | Research Director | 3 | Single | 13499 | 13782 | 9 | Y | No | 17 | 3 | 3 | 80 | 0 | 20 | 3 | 2 | 18 | 7 | 2 | 13 |
| 42 | NA | Travel_Frequently | 481 | Sales | 12 | 3 | Life Sciences | 1 | 1167 | 3 | Male | 44 | 3 | 4 | Sales Executive | 1 | Single | 13758 | 2447 | 0 | Y | Yes | 12 | 3 | 2 | 80 | 0 | 22 | 2 | 2 | 21 | 9 | 13 | 14 |
| 42 | No | Travel_Rarely | 647 | Sales | 4 | 4 | Marketing | 1 | 1171 | 2 | Male | 45 | 3 | 2 | Sales Executive | 1 | Single | 5155 | 2253 | 7 | Y | No | 13 | 3 | 4 | 80 | 0 | 9 | 3 | 4 | 6 | 4 | 1 | 5 |
| 35 | No | Travel_Rarely | 982 | Research & Development | 1 | 4 | Medical | 1 | 1172 | 4 | Male | 58 | 2 | 1 | Laboratory Technician | 3 | Married | 2258 | 16340 | 6 | Y | No | 12 | 3 | 2 | 80 | 1 | 10 | 2 | 3 | 8 | 0 | 1 | 7 |
| 24 | No | Travel_Rarely | 477 | Research & Development | 24 | 3 | Medical | 1 | 1173 | 4 | Male | 49 | 3 | 1 | Laboratory Technician | 2 | Single | 3597 | 6409 | 8 | Y | No | 22 | 4 | 4 | 80 | 0 | 6 | 2 | 3 | 4 | 3 | 1 | 2 |
| 28 | Yes | Travel_Rarely | 1485 | Research & Development | 12 | 1 | Life Sciences | 1 | 1175 | 3 | Female | 79 | 3 | 1 | Laboratory Technician | 4 | Married | 2515 | 22955 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 1 | 4 | 2 | 1 | 1 | 0 | 0 |
| 26 | No | Travel_Rarely | 1384 | Research & Development | 3 | 4 | Medical | 1 | 1177 | 1 | Male | 82 | 4 | 1 | Laboratory Technician | 4 | Married | 4420 | 13421 | 1 | Y | No | 22 | 4 | 2 | 80 | 1 | 8 | 2 | 3 | 8 | 7 | 0 | 7 |
| 30 | No | Travel_Rarely | 852 | Sales | 10 | 3 | Marketing | 1 | 1179 | 3 | Male | 72 | 2 | 2 | Sales Executive | 3 | Married | 6578 | 2706 | 1 | Y | No | 18 | 3 | 1 | 80 | 1 | 10 | 3 | 3 | 10 | 3 | 1 | 4 |
| 40 | No | Travel_Frequently | 902 | Research & Development | 26 | 2 | Medical | 1 | 1180 | 3 | Female | 92 | 2 | 2 | Research Scientist | 4 | Married | 4422 | 21203 | 3 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 16 | 3 | 1 | 1 | 1 | 0 | 0 |
| 35 | No | Travel_Rarely | 819 | Research & Development | 2 | 3 | Life Sciences | 1 | 1182 | 3 | Male | 44 | 2 | 3 | Manufacturing Director | 2 | Divorced | 10274 | 19588 | 2 | Y | No | 18 | 3 | 2 | 80 | 1 | 15 | 2 | 4 | 7 | 7 | 6 | 4 |
| 34 | No | Travel_Frequently | 669 | Research & Development | 1 | 3 | Medical | 1 | 1184 | 4 | Male | 97 | 2 | 2 | Healthcare Representative | 1 | Single | NA | 25755 | 0 | Y | No | 20 | 4 | 3 | 80 | 0 | 14 | 3 | 3 | 13 | 9 | 4 | 9 |
| 35 | No | Travel_Frequently | 636 | Research & Development | 4 | 4 | Other | 1 | 1185 | 4 | Male | 47 | 2 | 1 | Laboratory Technician | 4 | Married | 2376 | 26537 | 1 | Y | No | 13 | 3 | 2 | 80 | 1 | 2 | 2 | 4 | 2 | 2 | 2 | 2 |
| 43 | Yes | Travel_Rarely | 1372 | Sales | 9 | 3 | Marketing | 1 | 1188 | 1 | Female | 85 | 1 | 2 | Sales Executive | 3 | Single | 5346 | 9489 | 8 | Y | No | 13 | 3 | 2 | 80 | 0 | 7 | 2 | 2 | 4 | 3 | 1 | 3 |
| 32 | No | Non-Travel | 862 | Sales | 2 | 1 | Life Sciences | 1 | 1190 | 3 | Female | 76 | 3 | 1 | Sales Representative | 1 | Divorced | 2827 | 14947 | 1 | Y | No | 12 | 3 | 3 | 80 | 3 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 56 | No | Travel_Rarely | 718 | Research & Development | 4 | 4 | Technical Degree | 1 | 1191 | 4 | Female | 92 | 3 | 5 | Manager | 1 | Divorced | 19943 | 18575 | 4 | Y | No | 13 | 3 | 4 | 80 | 1 | 28 | 2 | 3 | 5 | 2 | 4 | 2 |
| 29 | No | Travel_Rarely | 1401 | Research & Development | 6 | 1 | Medical | 1 | 1192 | 2 | Female | 54 | 3 | 1 | Laboratory Technician | 4 | Married | 3131 | 26342 | 1 | Y | No | 13 | 3 | 1 | 80 | 1 | 10 | 5 | 3 | 10 | 8 | 0 | 8 |
| NA | No | Travel_Rarely | 645 | Research & Development | 9 | 2 | Life Sciences | 1 | 1193 | 3 | Male | 54 | 3 | 1 | Research Scientist | 1 | Single | 2552 | 7172 | 1 | Y | No | 25 | 4 | 3 | 80 | 0 | 1 | 4 | 3 | 1 | 1 | 0 | 0 |
| 45 | No | Travel_Rarely | 1457 | Research & Development | 7 | 3 | Medical | 1 | 1195 | 1 | Female | 83 | 3 | 1 | Research Scientist | 3 | Married | 4477 | 20100 | 4 | Y | Yes | 19 | 3 | 3 | 80 | 1 | 7 | 2 | 2 | 3 | 2 | 0 | 2 |
| 37 | No | Travel_Rarely | 977 | Research & Development | 1 | 3 | Life Sciences | 1 | 1196 | 4 | Female | 56 | 2 | 2 | Manufacturing Director | 4 | Married | 6474 | 9961 | 1 | Y | No | 13 | 3 | 2 | 80 | 1 | 14 | 2 | 2 | 14 | 8 | 3 | 11 |
| 20 | No | Travel_Rarely | 805 | Research & Development | 3 | 3 | Life Sciences | 1 | 1198 | 1 | Male | 87 | 2 | 1 | Laboratory Technician | 3 | Single | 3033 | 12828 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 2 | 2 | 2 | 2 | 2 | 1 | 2 |
| NA | Yes | Travel_Rarely | 1097 | Research & Development | 10 | 4 | Life Sciences | 1 | 1200 | 3 | Male | 96 | 3 | 1 | Research Scientist | 3 | Single | 2936 | 10826 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 6 | 4 | 3 | 6 | 4 | 0 | 2 |
| 53 | No | Travel_Rarely | 1223 | Research & Development | 7 | 2 | Medical | 1 | 1201 | 4 | Female | 50 | 3 | 5 | Manager | 3 | Divorced | 18606 | 18640 | 3 | Y | No | 18 | 3 | 2 | 80 | 1 | 26 | 6 | 3 | 7 | 7 | 4 | 7 |
| 29 | No | Travel_Rarely | 942 | Research & Development | 15 | 1 | Life Sciences | 1 | 1202 | 2 | Female | 69 | 1 | 1 | Research Scientist | 4 | Married | 2168 | 26933 | 0 | Y | Yes | 18 | 3 | 1 | 80 | 1 | 6 | 2 | 2 | 5 | 4 | 1 | 3 |
| 22 | Yes | Travel_Frequently | 1256 | Research & Development | 3 | 4 | Life Sciences | 1 | 1203 | 3 | Male | 48 | 2 | 1 | Research Scientist | 4 | Married | 2853 | 4223 | 0 | Y | Yes | 11 | 3 | 2 | 80 | 1 | 1 | 5 | 3 | 0 | 0 | 0 | 0 |
| 46 | No | Travel_Rarely | 1402 | Sales | 2 | 3 | Marketing | 1 | 1204 | 3 | Female | 69 | 3 | 4 | Manager | 1 | Married | 17048 | 24097 | 8 | Y | No | 23 | 4 | 1 | 80 | 0 | 28 | 2 | 3 | 26 | 15 | 15 | 9 |
| 44 | No | Non-Travel | 111 | Research & Development | 17 | 3 | Life Sciences | 1 | 1206 | 4 | Male | 74 | 1 | 1 | Research Scientist | 3 | Single | NA | 4279 | 2 | Y | No | 13 | 3 | 4 | 80 | 0 | 6 | 3 | 3 | 0 | 0 | 0 | 0 |
| 33 | No | Travel_Rarely | 147 | Human Resources | 2 | 3 | Human Resources | 1 | 1207 | 2 | Male | 99 | 3 | 1 | Human Resources | 3 | Married | 3600 | 8429 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 5 | 2 | 3 | 5 | 4 | 1 | 4 |
| 41 | Yes | Non-Travel | 906 | Research & Development | 5 | 2 | Life Sciences | 1 | 1210 | 1 | Male | 95 | 2 | 1 | Research Scientist | 1 | Divorced | 2107 | 20293 | 6 | Y | No | 17 | 3 | 1 | 80 | 1 | 5 | 2 | 1 | 1 | 0 | 0 | 0 |
| 30 | NA | Travel_Rarely | 1329 | Sales | 29 | 4 | Life Sciences | 1 | 1211 | 3 | Male | 61 | 3 | 2 | Sales Executive | 1 | Divorced | 4115 | 13192 | 8 | Y | No | 19 | 3 | 3 | 80 | 3 | 8 | 3 | 3 | 4 | 3 | 0 | 3 |
| 40 | No | Travel_Frequently | 1184 | Sales | 2 | 4 | Medical | 1 | 1212 | 2 | Male | 62 | 3 | 2 | Sales Executive | 2 | Married | 4327 | 25440 | 5 | Y | No | 12 | 3 | 4 | 80 | 3 | 5 | 2 | 3 | 0 | 0 | 0 | 0 |
| 50 | No | Travel_Frequently | 1421 | Research & Development | 2 | 3 | Medical | 1 | 1215 | 4 | Female | 30 | 3 | 4 | Manager | 1 | Married | 17856 | 9490 | 2 | Y | No | 22 | 4 | 3 | 80 | 1 | 32 | 3 | 3 | 2 | 2 | 2 | 2 |
| 28 | No | Travel_Rarely | 1179 | Research & Development | 19 | 4 | Medical | 1 | 1216 | 4 | Male | 78 | 2 | 1 | Laboratory Technician | 1 | Married | 3196 | 12449 | 1 | Y | No | 12 | 3 | 3 | 80 | 3 | 6 | 2 | 3 | 6 | 5 | 3 | 3 |
| 46 | No | Travel_Rarely | 1450 | Research & Development | 15 | 2 | Life Sciences | 1 | 1217 | 4 | Male | 52 | 3 | 5 | Research Director | 2 | Married | 19081 | 10849 | 5 | Y | No | 11 | 3 | 1 | 80 | 1 | 25 | 2 | 3 | 4 | 2 | 0 | 3 |
| 35 | No | Travel_Rarely | 1361 | Sales | 17 | 4 | Life Sciences | 1 | 1218 | 3 | Male | 94 | 3 | 2 | Sales Executive | 1 | Married | 8966 | 21026 | 3 | Y | Yes | 15 | 3 | 4 | 80 | 3 | 15 | 2 | 3 | 7 | 7 | 1 | 7 |
| 24 | Yes | Travel_Rarely | 984 | Research & Development | 17 | 2 | Life Sciences | 1 | 1219 | 4 | Female | 97 | 3 | 1 | Laboratory Technician | 2 | Married | 2210 | 3372 | 1 | Y | No | 13 | 3 | 1 | 80 | 1 | 1 | 3 | 1 | 1 | 0 | 0 | 0 |
| 33 | NA | Travel_Frequently | 1146 | Sales | 25 | 3 | Medical | 1 | 1220 | 2 | Female | 82 | 3 | 2 | Sales Executive | 3 | Married | NA | 4905 | 1 | Y | No | 12 | 3 | 1 | 80 | 1 | 10 | 3 | 2 | 10 | 7 | 0 | 1 |
| 36 | No | Travel_Rarely | 917 | Research & Development | 6 | 4 | Life Sciences | 1 | 1221 | 3 | Male | 60 | 1 | 1 | Laboratory Technician | 3 | Divorced | 2741 | 6865 | 1 | Y | No | 14 | 3 | 3 | 80 | 1 | 7 | 4 | 3 | 7 | 7 | 1 | 7 |
| 30 | No | Travel_Rarely | 853 | Research & Development | 7 | 4 | Life Sciences | 1 | 1224 | 3 | Male | 49 | 3 | 2 | Laboratory Technician | 3 | Divorced | 3491 | 11309 | 1 | Y | No | 13 | 3 | 1 | 80 | 3 | 10 | 4 | 2 | 10 | 7 | 8 | 9 |
| 44 | No | Travel_Rarely | 200 | Research & Development | 29 | 4 | Other | 1 | 1225 | 4 | Male | 32 | 3 | 2 | Research Scientist | 4 | Single | 4541 | 7744 | 1 | Y | No | 25 | 4 | 2 | 80 | 0 | 20 | 3 | 3 | 20 | 11 | 13 | 17 |
| 20 | No | Travel_Rarely | 654 | Sales | 21 | 3 | Marketing | 1 | 1226 | 3 | Male | 43 | 4 | 1 | Sales Representative | 4 | Single | 2678 | 5050 | 1 | Y | No | 17 | 3 | 4 | 80 | 0 | 2 | 2 | 3 | 2 | 1 | 2 | 2 |
| 46 | No | Travel_Rarely | 150 | Research & Development | 2 | 4 | Technical Degree | 1 | 1228 | 4 | Male | 60 | 3 | 2 | Manufacturing Director | 4 | Divorced | 7379 | 17433 | 2 | Y | No | 11 | 3 | 3 | 80 | 1 | 12 | 3 | 2 | 6 | 3 | 1 | 4 |
| 42 | No | Non-Travel | 179 | Human Resources | 2 | 5 | Medical | 1 | 1231 | 4 | Male | 79 | 4 | 2 | Human Resources | 1 | Married | 6272 | 12858 | 7 | Y | No | 16 | 3 | 1 | 80 | 1 | 10 | 3 | 4 | 4 | 3 | 0 | 3 |
| 60 | No | Travel_Rarely | 696 | Sales | 7 | 4 | Marketing | 1 | 1233 | 2 | Male | 52 | 4 | 2 | Sales Executive | 4 | Divorced | 5220 | 10893 | 0 | Y | Yes | 18 | 3 | 2 | 80 | 1 | 12 | 3 | 3 | 11 | 7 | 1 | 9 |
| NA | NA | Travel_Frequently | 116 | Research & Development | 13 | 3 | Other | 1 | 1234 | 3 | Female | 77 | 2 | 1 | Laboratory Technician | 2 | Married | 2743 | 7331 | 1 | Y | No | 20 | 4 | 3 | 80 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 |
| 32 | No | Travel_Frequently | 1316 | Research & Development | 2 | 2 | Life Sciences | 1 | 1235 | 4 | Female | 38 | 3 | 2 | Research Scientist | 3 | Single | 4998 | 2338 | 4 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 8 | 7 | 0 | 7 |
| 36 | No | Travel_Rarely | 363 | Research & Development | 1 | 3 | Technical Degree | 1 | 1237 | 3 | Female | 77 | 1 | 3 | Manufacturing Director | 1 | Divorced | 10252 | 4235 | 2 | Y | Yes | 21 | 4 | 3 | 80 | 1 | 17 | 2 | 3 | 7 | 7 | 7 | 7 |
| 33 | No | Travel_Rarely | 117 | Research & Development | 9 | 3 | Medical | 1 | 1238 | 1 | Male | 60 | 3 | 1 | Research Scientist | 4 | Married | NA | 6311 | 0 | Y | No | 13 | 3 | 2 | 80 | 1 | 15 | 5 | 3 | 14 | 10 | 4 | 10 |
| 40 | No | Travel_Rarely | 107 | Sales | 10 | 3 | Technical Degree | 1 | 1239 | 2 | Female | 84 | 2 | 2 | Sales Executive | 2 | Divorced | 6852 | 11591 | 7 | Y | No | 12 | 3 | 2 | 80 | 1 | 7 | 2 | 4 | 5 | 1 | 1 | 3 |
| 25 | No | Travel_Rarely | 1356 | Sales | 10 | 4 | Life Sciences | 1 | 1240 | 3 | Male | 57 | 3 | 2 | Sales Executive | 4 | Single | 4950 | 20623 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 5 | 4 | 3 | 4 | 3 | 1 | 1 |
| 30 | No | Travel_Rarely | 1465 | Research & Development | 1 | 3 | Medical | 1 | 1241 | 4 | Male | 63 | 3 | 1 | Research Scientist | 2 | Married | 3579 | 9369 | 0 | Y | Yes | 21 | 4 | 1 | 80 | 1 | 12 | 2 | 3 | 11 | 9 | 5 | 7 |
| 42 | No | Travel_Frequently | 458 | Research & Development | 26 | 5 | Medical | 1 | 1242 | 1 | Female | 60 | 3 | 3 | Research Director | 1 | Married | 13191 | 23281 | 3 | Y | Yes | 17 | 3 | 3 | 80 | 0 | 20 | 6 | 3 | 1 | 0 | 0 | 0 |
| 35 | No | Non-Travel | 1212 | Sales | 8 | 2 | Marketing | 1 | 1243 | 3 | Female | 78 | 2 | 3 | Sales Executive | 4 | Married | 10377 | 13755 | 4 | Y | Yes | 11 | 3 | 2 | 80 | 1 | 16 | 6 | 2 | 13 | 2 | 4 | 12 |
| 27 | No | Travel_Rarely | 1103 | Research & Development | 14 | 3 | Life Sciences | 1 | 1244 | 1 | Male | 42 | 3 | 1 | Research Scientist | 1 | Married | 2235 | 14377 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 2 | 9 | 3 | 2 | 9 | 7 | 6 | 8 |
| 54 | No | Travel_Frequently | 966 | Research & Development | 1 | 4 | Life Sciences | 1 | 1245 | 4 | Female | 53 | 3 | 3 | Manufacturing Director | 3 | Divorced | 10502 | 9659 | 7 | Y | No | 17 | 3 | 1 | 80 | 1 | 33 | 2 | 1 | 5 | 4 | 1 | 4 |
| 44 | No | Travel_Rarely | 1117 | Research & Development | 2 | 1 | Life Sciences | 1 | 1246 | 1 | Female | 72 | 4 | 1 | Research Scientist | 4 | Married | 2011 | 19982 | 1 | Y | No | 13 | 3 | 4 | 80 | 1 | 10 | 5 | 3 | 10 | 5 | 7 | 7 |
| 19 | Yes | Non-Travel | 504 | Research & Development | 10 | 3 | Medical | 1 | 1248 | 1 | Female | 96 | 2 | 1 | Research Scientist | 2 | Single | 1859 | 6148 | 1 | Y | Yes | 25 | 4 | 2 | 80 | 0 | 1 | 2 | 4 | 1 | 1 | 0 | 0 |
| 29 | No | Travel_Rarely | 1010 | Research & Development | 1 | 3 | Life Sciences | 1 | 1249 | 1 | Female | 97 | 3 | 1 | Research Scientist | 4 | Divorced | 3760 | 5598 | 1 | Y | No | 15 | 3 | 1 | 80 | 3 | 3 | 5 | 3 | 3 | 2 | 1 | 2 |
| 54 | No | Travel_Rarely | 685 | Research & Development | 3 | 3 | Life Sciences | 1 | 1250 | 4 | Male | 85 | 3 | 4 | Research Director | 4 | Married | 17779 | 23474 | 3 | Y | No | 14 | 3 | 1 | 80 | 0 | 36 | 2 | 3 | 10 | 9 | 0 | 9 |
| 31 | No | Travel_Rarely | 1332 | Research & Development | 11 | 2 | Medical | 1 | 1251 | 3 | Male | 80 | 3 | 2 | Healthcare Representative | 1 | Married | 6833 | 17089 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 6 | 2 | 2 | 6 | 5 | 0 | 1 |
| 31 | No | Travel_Rarely | 1062 | Research & Development | 24 | 3 | Medical | 1 | 1252 | 3 | Female | 96 | 2 | 2 | Healthcare Representative | 1 | Single | 6812 | 17198 | 1 | Y | No | 19 | 3 | 2 | 80 | 0 | 10 | 2 | 3 | 10 | 9 | 1 | 8 |
| 59 | No | Travel_Rarely | 326 | Sales | 3 | 3 | Life Sciences | 1 | 1254 | 3 | Female | 48 | 2 | 2 | Sales Executive | 4 | Single | 5171 | 16490 | 5 | Y | No | 17 | 3 | 4 | 80 | 0 | 13 | 2 | 3 | 6 | 1 | 0 | 5 |
| 43 | No | Travel_Rarely | 920 | Research & Development | 3 | 3 | Life Sciences | 1 | 1255 | 3 | Male | 96 | 1 | 5 | Research Director | 4 | Married | 19740 | 18625 | 3 | Y | No | 14 | 3 | 2 | 80 | 1 | 25 | 2 | 3 | 8 | 7 | 0 | 7 |
| 49 | No | Travel_Rarely | 1098 | Research & Development | 4 | 2 | Medical | 1 | 1256 | 1 | Male | 85 | 2 | 5 | Manager | 3 | Married | 18711 | 12124 | 2 | Y | No | 13 | 3 | 3 | 80 | 1 | 23 | 2 | 4 | 1 | 0 | 0 | 0 |
| NA | No | Travel_Frequently | 469 | Research & Development | 3 | 3 | Technical Degree | 1 | 1257 | 3 | Male | 46 | 3 | 1 | Research Scientist | 2 | Married | 3692 | 9256 | 1 | Y | No | 12 | 3 | 3 | 80 | 0 | 12 | 2 | 2 | 11 | 10 | 0 | 7 |
| 48 | No | Travel_Rarely | 969 | Research & Development | 2 | 2 | Technical Degree | 1 | 1258 | 4 | Male | 76 | 4 | 1 | Laboratory Technician | 2 | Single | 2559 | 16620 | 5 | Y | No | 11 | 3 | 3 | 80 | 0 | 7 | 4 | 2 | 1 | 0 | 0 | 0 |
| 27 | No | Travel_Rarely | 1167 | Research & Development | 4 | 2 | Life Sciences | 1 | 1259 | 1 | Male | 76 | 3 | 1 | Research Scientist | 3 | Divorced | 2517 | 3208 | 1 | Y | No | 11 | 3 | 2 | 80 | 3 | 5 | 2 | 3 | 5 | 3 | 0 | 3 |
| 29 | NA | Travel_Rarely | 1329 | Research & Development | 7 | 3 | Life Sciences | 1 | 1260 | 3 | Male | 82 | 3 | 2 | Healthcare Representative | 4 | Divorced | 6623 | 4204 | 1 | Y | Yes | 11 | 3 | 2 | 80 | 2 | 6 | 2 | 3 | 6 | 0 | 1 | 0 |
| NA | No | Travel_Rarely | 715 | Research & Development | 1 | 3 | Life Sciences | 1 | 1263 | 4 | Male | 76 | 2 | 5 | Research Director | 4 | Single | 18265 | 8733 | 6 | Y | No | 12 | 3 | 3 | 80 | 0 | 25 | 3 | 4 | 1 | 0 | 0 | 0 |
| 29 | No | Travel_Rarely | 694 | Research & Development | 1 | 3 | Life Sciences | 1 | 1264 | 4 | Female | 87 | 2 | 4 | Research Director | 4 | Divorced | 16124 | 3423 | 3 | Y | No | 14 | 3 | 2 | 80 | 2 | 9 | 2 | 2 | 7 | 7 | 1 | 7 |
| 34 | NA | Travel_Rarely | 1320 | Research & Development | 20 | 3 | Technical Degree | 1 | 1265 | 3 | Female | 89 | 4 | 1 | Research Scientist | 3 | Married | 2585 | 21643 | 0 | Y | No | 17 | 3 | 4 | 80 | 0 | 2 | 5 | 2 | 1 | 0 | 0 | 0 |
| 44 | No | Travel_Rarely | 1099 | Sales | 5 | 3 | Marketing | 1 | 1267 | 2 | Male | 88 | 3 | 5 | Manager | 2 | Married | 18213 | 8751 | 7 | Y | No | 11 | 3 | 3 | 80 | 1 | 26 | 5 | 3 | 22 | 9 | 3 | 10 |
| 33 | No | Travel_Rarely | 536 | Sales | 10 | 5 | Marketing | 1 | 1268 | 4 | Male | 82 | 4 | 3 | Sales Executive | 3 | Divorced | 8380 | 21708 | 0 | Y | Yes | 14 | 3 | 4 | 80 | 2 | 10 | 3 | 3 | 9 | 8 | 0 | 8 |
| 19 | No | Travel_Rarely | 265 | Research & Development | 25 | 3 | Life Sciences | 1 | 1269 | 2 | Female | 57 | 4 | 1 | Research Scientist | 4 | Single | 2994 | 21221 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 1 |
| 23 | No | Travel_Rarely | 373 | Research & Development | 1 | 2 | Life Sciences | 1 | 1270 | 4 | Male | 47 | 3 | 1 | Research Scientist | 3 | Married | 1223 | 16901 | 1 | Y | No | 22 | 4 | 4 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 1 |
| 25 | Yes | Travel_Frequently | 599 | Sales | 24 | 1 | Life Sciences | 1 | 1273 | 3 | Male | 73 | 1 | 1 | Sales Representative | 4 | Single | NA | 8040 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 1 | 4 | 3 | 1 | 0 | 1 | 0 |
| 26 | No | Travel_Rarely | 583 | Research & Development | 4 | 2 | Life Sciences | 1 | 1275 | 3 | Male | 53 | 3 | 1 | Research Scientist | 4 | Single | 2875 | 9973 | 1 | Y | Yes | 20 | 4 | 2 | 80 | 0 | 8 | 2 | 2 | 8 | 5 | 2 | 2 |
| 45 | Yes | Travel_Rarely | 1449 | Sales | 2 | 3 | Marketing | 1 | 1277 | 1 | Female | 94 | 1 | 5 | Manager | 2 | Single | 18824 | 2493 | 2 | Y | Yes | 16 | 3 | 1 | 80 | 0 | 26 | 2 | 3 | 24 | 10 | 1 | 11 |
| 55 | No | Non-Travel | 177 | Research & Development | 8 | 1 | Medical | 1 | 1278 | 4 | Male | 37 | 2 | 4 | Healthcare Representative | 2 | Divorced | 13577 | 25592 | 1 | Y | Yes | 15 | 3 | 4 | 80 | 1 | 34 | 3 | 3 | 33 | 9 | 15 | 0 |
| 21 | Yes | Travel_Frequently | 251 | Research & Development | 10 | 2 | Life Sciences | 1 | 1279 | 1 | Female | 45 | 2 | 1 | Laboratory Technician | 3 | Single | 2625 | 25308 | 1 | Y | No | 20 | 4 | 3 | 80 | 0 | 2 | 2 | 1 | 2 | 2 | 2 | 2 |
| 46 | No | Travel_Rarely | 168 | Sales | 4 | 2 | Marketing | 1 | 1280 | 4 | Female | 33 | 2 | 5 | Manager | 2 | Married | NA | 9946 | 2 | Y | No | 14 | 3 | 3 | 80 | 1 | 26 | 2 | 3 | 11 | 4 | 0 | 8 |
| 34 | No | Travel_Rarely | 131 | Sales | 2 | 3 | Marketing | 1 | 1281 | 3 | Female | 86 | 3 | 2 | Sales Executive | 1 | Single | 4538 | 6039 | 0 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 4 | 3 | 3 | 3 | 2 | 0 | 2 |
| 51 | No | Travel_Frequently | 237 | Sales | 9 | 3 | Life Sciences | 1 | 1282 | 4 | Male | 83 | 3 | 5 | Manager | 2 | Divorced | 19847 | 19196 | 4 | Y | Yes | 24 | 4 | 1 | 80 | 1 | 31 | 5 | 2 | 29 | 10 | 11 | 10 |
| 59 | No | Travel_Rarely | 1429 | Research & Development | 18 | 4 | Medical | 1 | 1283 | 4 | Male | 67 | 3 | 3 | Manufacturing Director | 4 | Single | 10512 | 20002 | 6 | Y | No | 12 | 3 | 4 | 80 | 0 | 25 | 6 | 2 | 9 | 7 | 5 | 4 |
| 34 | No | Travel_Frequently | 135 | Research & Development | 19 | 3 | Medical | 1 | 1285 | 3 | Female | 46 | 3 | 2 | Laboratory Technician | 2 | Divorced | 4444 | 22534 | 4 | Y | No | 13 | 3 | 3 | 80 | 2 | 15 | 2 | 4 | 11 | 8 | 5 | 10 |
| 28 | No | Travel_Frequently | 791 | Research & Development | 1 | 4 | Medical | 1 | 1286 | 4 | Male | 44 | 3 | 1 | Laboratory Technician | 3 | Single | 2154 | 6842 | 0 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 5 | 2 | 2 | 4 | 2 | 0 | 2 |
| 44 | No | Travel_Rarely | 1199 | Research & Development | 4 | 2 | Life Sciences | 1 | 1288 | 3 | Male | 92 | 4 | 5 | Manager | 1 | Divorced | 19190 | 17477 | 1 | Y | No | 14 | 3 | 4 | 80 | 2 | 26 | 4 | 2 | 25 | 9 | 14 | 13 |
| 34 | NA | Travel_Frequently | 648 | Human Resources | 11 | 3 | Life Sciences | 1 | 1289 | 3 | Male | 56 | 2 | 2 | Human Resources | 2 | Married | 4490 | 21833 | 4 | Y | No | 11 | 3 | 4 | 80 | 2 | 14 | 5 | 4 | 10 | 9 | 1 | 8 |
| 35 | No | Travel_Rarely | 735 | Research & Development | 6 | 1 | Life Sciences | 1 | 1291 | 3 | Male | 66 | 3 | 1 | Research Scientist | 3 | Married | 3506 | 6020 | 0 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 4 | 3 | 3 | 3 | 2 | 2 | 2 |
| 42 | No | Travel_Rarely | 603 | Research & Development | 7 | 4 | Medical | 1 | 1292 | 2 | Female | 78 | 4 | 2 | Research Scientist | 2 | Married | 2372 | 5628 | 6 | Y | Yes | 16 | 3 | 4 | 80 | 0 | 18 | 2 | 3 | 1 | 0 | 0 | 0 |
| 43 | No | Travel_Rarely | 531 | Sales | 4 | 4 | Marketing | 1 | 1293 | 4 | Female | 56 | 2 | 3 | Sales Executive | 4 | Single | NA | 20364 | 3 | Y | No | 14 | 3 | 4 | 80 | 0 | 23 | 3 | 4 | 21 | 7 | 15 | 17 |
| NA | No | Travel_Rarely | 429 | Research & Development | 2 | 4 | Life Sciences | 1 | 1294 | 3 | Female | 53 | 3 | 2 | Manufacturing Director | 2 | Single | 5410 | 2323 | 9 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 18 | 2 | 3 | 16 | 14 | 5 | 12 |
| 44 | NA | Travel_Rarely | 621 | Research & Development | 15 | 3 | Medical | 1 | 1295 | 1 | Female | 73 | 3 | 3 | Healthcare Representative | 4 | Married | 7978 | 14075 | 1 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 2 | 3 | 10 | 7 | 0 | 5 |
| 28 | NA | Travel_Frequently | 193 | Research & Development | 2 | 3 | Life Sciences | 1 | 1296 | 4 | Male | 52 | 2 | 1 | Laboratory Technician | 4 | Married | 3867 | 14222 | 1 | Y | Yes | 12 | 3 | 2 | 80 | 1 | 2 | 2 | 3 | 2 | 2 | 2 | 2 |
| 51 | No | Travel_Frequently | 968 | Research & Development | 6 | 2 | Medical | 1 | 1297 | 2 | Female | 40 | 2 | 1 | Laboratory Technician | 3 | Single | 2838 | 4257 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 8 | 6 | 2 | 7 | 0 | 7 | 7 |
| 30 | No | Non-Travel | 879 | Research & Development | 9 | 2 | Medical | 1 | 1298 | 3 | Female | 72 | 3 | 2 | Manufacturing Director | 3 | Single | 4695 | 12858 | 7 | Y | Yes | 18 | 3 | 3 | 80 | 0 | 10 | 3 | 3 | 8 | 4 | 1 | 7 |
| 29 | Yes | Travel_Rarely | 806 | Research & Development | 7 | 3 | Technical Degree | 1 | 1299 | 2 | Female | 39 | 3 | 1 | Laboratory Technician | 3 | Divorced | 3339 | 17285 | 3 | Y | Yes | 13 | 3 | 1 | 80 | 2 | 10 | 2 | 3 | 7 | 7 | 7 | 7 |
| 28 | No | Travel_Rarely | 640 | Research & Development | 1 | 3 | Technical Degree | 1 | 1301 | 4 | Male | 84 | 3 | 1 | Research Scientist | 1 | Single | 2080 | 4732 | 2 | Y | No | 11 | 3 | 2 | 80 | 0 | 5 | 2 | 2 | 3 | 2 | 1 | 2 |
| 25 | NA | Travel_Rarely | 266 | Research & Development | 1 | 3 | Medical | 1 | 1303 | 4 | Female | 40 | 3 | 1 | Research Scientist | 2 | Single | 2096 | 18830 | 1 | Y | No | 18 | 3 | 4 | 80 | 0 | 2 | 3 | 2 | 2 | 2 | 2 | 1 |
| 32 | No | Travel_Rarely | 604 | Sales | 8 | 3 | Medical | 1 | 1304 | 3 | Male | 56 | 4 | 2 | Sales Executive | 4 | Married | 6209 | 11693 | 1 | Y | No | 15 | 3 | 3 | 80 | 2 | 10 | 4 | 4 | 10 | 7 | 0 | 8 |
| 45 | No | Travel_Frequently | 364 | Research & Development | 25 | 3 | Medical | 1 | 1306 | 2 | Female | 83 | 3 | 5 | Manager | 2 | Single | 18061 | 13035 | 3 | Y | No | 22 | 4 | 3 | 80 | 0 | 22 | 4 | 3 | 0 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 412 | Research & Development | 13 | 4 | Medical | 1 | 1307 | 3 | Female | 94 | 2 | 4 | Manager | 2 | Divorced | 17123 | 17334 | 6 | Y | Yes | 13 | 3 | 4 | 80 | 2 | 21 | 4 | 3 | 19 | 9 | 15 | 2 |
| 58 | No | Travel_Rarely | 848 | Research & Development | 23 | 4 | Life Sciences | 1 | 1308 | 1 | Male | 88 | 3 | 1 | Research Scientist | 3 | Divorced | 2372 | 26076 | 1 | Y | No | 12 | 3 | 4 | 80 | 2 | 2 | 3 | 3 | 2 | 2 | 2 | 2 |
| 32 | Yes | Travel_Rarely | 1089 | Research & Development | 7 | 2 | Life Sciences | 1 | 1309 | 4 | Male | 79 | 3 | 2 | Laboratory Technician | 3 | Married | 4883 | 22845 | 1 | Y | No | 18 | 3 | 1 | 80 | 1 | 10 | 3 | 3 | 10 | 4 | 1 | 1 |
| 39 | Yes | Travel_Rarely | 360 | Research & Development | 23 | 3 | Medical | 1 | 1310 | 3 | Male | 93 | 3 | 1 | Research Scientist | 1 | Single | 3904 | 22154 | 0 | Y | No | 13 | 3 | 1 | 80 | 0 | 6 | 2 | 3 | 5 | 2 | 0 | 3 |
| 30 | No | Travel_Rarely | 1138 | Research & Development | 6 | 3 | Technical Degree | 1 | 1311 | 1 | Female | 48 | 2 | 2 | Laboratory Technician | 4 | Married | 4627 | 23631 | 0 | Y | No | 12 | 3 | 1 | 80 | 1 | 10 | 6 | 3 | 9 | 2 | 6 | 7 |
| 36 | No | Travel_Rarely | 325 | Research & Development | 10 | 4 | Technical Degree | 1 | 1312 | 4 | Female | 63 | 3 | 3 | Healthcare Representative | 3 | Married | 7094 | 5747 | 3 | Y | No | 12 | 3 | 1 | 80 | 0 | 10 | 0 | 3 | 7 | 7 | 1 | 7 |
| 46 | No | Travel_Rarely | 991 | Human Resources | 1 | 2 | Life Sciences | 1 | 1314 | 4 | Female | 44 | 3 | 1 | Human Resources | 1 | Single | 3423 | 22957 | 6 | Y | No | 12 | 3 | 3 | 80 | 0 | 10 | 3 | 4 | 7 | 6 | 5 | 7 |
| 28 | No | Non-Travel | 1476 | Research & Development | 1 | 3 | Life Sciences | 1 | 1315 | 3 | Female | 55 | 1 | 2 | Laboratory Technician | 4 | Married | 6674 | 16392 | 0 | Y | No | 11 | 3 | 1 | 80 | 3 | 10 | 6 | 3 | 9 | 8 | 7 | 5 |
| 50 | No | Travel_Rarely | 1322 | Research & Development | 28 | 3 | Life Sciences | 1 | 1317 | 4 | Female | 43 | 3 | 4 | Research Director | 1 | Married | 16880 | 22422 | 4 | Y | Yes | 11 | 3 | 2 | 80 | 0 | 25 | 2 | 3 | 3 | 2 | 1 | 2 |
| 40 | Yes | Travel_Rarely | 299 | Sales | 25 | 4 | Marketing | 1 | 1318 | 4 | Male | 57 | 2 | 3 | Sales Executive | 2 | Single | NA | 17235 | 2 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 9 | 2 | 3 | 5 | 4 | 1 | 0 |
| 52 | NA | Travel_Rarely | 1030 | Sales | 5 | 3 | Life Sciences | 1 | 1319 | 2 | Male | 64 | 3 | 3 | Sales Executive | 2 | Single | 8446 | 21534 | 9 | Y | Yes | 19 | 3 | 3 | 80 | 0 | 10 | 2 | 2 | 8 | 7 | 7 | 7 |
| NA | No | Travel_Rarely | 634 | Research & Development | 17 | 4 | Medical | 1 | 1321 | 2 | Female | 95 | 3 | 3 | Manager | 1 | Married | 11916 | 25927 | 1 | Y | Yes | 23 | 4 | 4 | 80 | 2 | 9 | 2 | 3 | 9 | 1 | 0 | 8 |
| 39 | No | Travel_Rarely | 524 | Research & Development | 18 | 2 | Life Sciences | 1 | 1322 | 1 | Male | 32 | 3 | 2 | Manufacturing Director | 3 | Single | 4534 | 13352 | 0 | Y | No | 11 | 3 | 1 | 80 | 0 | 9 | 6 | 3 | 8 | 7 | 1 | 7 |
| 31 | No | Non-Travel | 587 | Sales | 2 | 4 | Life Sciences | 1 | 1324 | 4 | Female | 57 | 3 | 3 | Sales Executive | 3 | Divorced | NA | 8935 | 1 | Y | Yes | 19 | 3 | 1 | 80 | 1 | 10 | 5 | 2 | 10 | 8 | 9 | 6 |
| 41 | No | Non-Travel | 256 | Sales | 10 | 2 | Medical | 1 | 1329 | 3 | Male | 40 | 1 | 2 | Sales Executive | 2 | Single | 6151 | 22074 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 19 | 4 | 3 | 19 | 2 | 11 | 9 |
| 31 | Yes | Travel_Frequently | 1060 | Sales | 1 | 3 | Life Sciences | 1 | 1331 | 4 | Female | 54 | 3 | 1 | Sales Representative | 2 | Single | 2302 | 8319 | 1 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 3 | 2 | 4 | 3 | 2 | 2 | 2 |
| 44 | Yes | Travel_Rarely | 935 | Research & Development | 3 | 3 | Life Sciences | 1 | 1333 | 1 | Male | 89 | 3 | 1 | Laboratory Technician | 1 | Married | 2362 | 14669 | 4 | Y | No | 12 | 3 | 3 | 80 | 0 | 10 | 4 | 4 | 3 | 2 | 1 | 2 |
| 42 | NA | Non-Travel | 495 | Research & Development | 2 | 1 | Life Sciences | 1 | 1334 | 3 | Male | 37 | 3 | 4 | Manager | 3 | Married | 17861 | 26582 | 0 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 21 | 3 | 2 | 20 | 8 | 2 | 10 |
| 55 | NA | Travel_Rarely | 282 | Research & Development | 2 | 2 | Medical | 1 | 1336 | 4 | Female | 58 | 1 | 5 | Manager | 3 | Married | 19187 | 6992 | 4 | Y | No | 14 | 3 | 4 | 80 | 1 | 23 | 5 | 3 | 19 | 9 | 9 | 11 |
| 56 | No | Travel_Rarely | 206 | Human Resources | 8 | 4 | Life Sciences | 1 | 1338 | 4 | Male | 99 | 3 | 5 | Manager | 2 | Single | 19717 | 4022 | 6 | Y | No | 14 | 3 | 1 | 80 | 0 | 36 | 4 | 3 | 7 | 3 | 7 | 7 |
| 40 | NA | Non-Travel | 458 | Research & Development | 16 | 2 | Life Sciences | 1 | 1340 | 3 | Male | 74 | 3 | 1 | Research Scientist | 3 | Divorced | 3544 | 8532 | 9 | Y | No | 16 | 3 | 2 | 80 | 1 | 6 | 0 | 3 | 4 | 2 | 0 | 0 |
| 34 | No | Travel_Rarely | 943 | Research & Development | 9 | 3 | Life Sciences | 1 | 1344 | 4 | Male | 86 | 3 | 3 | Healthcare Representative | 4 | Divorced | 8500 | 5494 | 0 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 0 | 2 | 9 | 7 | 1 | 6 |
| 40 | No | Travel_Rarely | 523 | Research & Development | 2 | 3 | Life Sciences | 1 | 1346 | 3 | Male | 98 | 3 | 2 | Research Scientist | 4 | Single | 4661 | 22455 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 9 | 4 | 3 | 9 | 8 | 8 | 8 |
| 41 | No | Travel_Frequently | 1018 | Sales | 1 | 3 | Marketing | 1 | 1349 | 3 | Female | 66 | 3 | 2 | Sales Executive | 1 | Divorced | 4103 | 4297 | 0 | Y | No | 17 | 3 | 4 | 80 | 1 | 10 | 2 | 3 | 9 | 3 | 1 | 7 |
| 35 | No | Travel_Frequently | 482 | Research & Development | 4 | 4 | Life Sciences | 1 | 1350 | 3 | Male | 87 | 3 | 2 | Research Scientist | 3 | Single | 4249 | 2690 | 1 | Y | Yes | 11 | 3 | 2 | 80 | 0 | 9 | 3 | 3 | 9 | 6 | 1 | 1 |
| 51 | No | Travel_Rarely | 770 | Human Resources | 5 | 3 | Life Sciences | 1 | 1352 | 3 | Male | 84 | 3 | 4 | Manager | 2 | Divorced | 14026 | 17588 | 1 | Y | Yes | 11 | 3 | 2 | 80 | 1 | 33 | 2 | 3 | 33 | 9 | 0 | 10 |
| 38 | No | Travel_Rarely | 1009 | Sales | 2 | 2 | Life Sciences | 1 | 1355 | 2 | Female | 31 | 3 | 2 | Sales Executive | 1 | Divorced | 6893 | 19461 | 3 | Y | No | 15 | 3 | 4 | 80 | 1 | 11 | 3 | 3 | 7 | 7 | 1 | 7 |
| 34 | No | Travel_Rarely | 507 | Sales | 15 | 2 | Medical | 1 | 1356 | 3 | Female | 66 | 3 | 2 | Sales Executive | 1 | Single | 6125 | 23553 | 1 | Y | No | 12 | 3 | 4 | 80 | 0 | 10 | 6 | 4 | 10 | 8 | 9 | 6 |
| 25 | No | Travel_Rarely | 882 | Research & Development | 19 | 1 | Medical | 1 | 1358 | 4 | Male | 67 | 3 | 1 | Laboratory Technician | 4 | Married | 3669 | 9075 | 3 | Y | No | 11 | 3 | 3 | 80 | 3 | 7 | 6 | 2 | 3 | 2 | 1 | 2 |
| 58 | Yes | Travel_Rarely | 601 | Research & Development | 7 | 4 | Medical | 1 | 1360 | 3 | Female | 53 | 2 | 3 | Manufacturing Director | 1 | Married | 10008 | 12023 | 7 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 31 | 0 | 2 | 10 | 9 | 5 | 9 |
| 40 | No | Travel_Rarely | 329 | Research & Development | 1 | 4 | Life Sciences | 1 | 1361 | 2 | Male | 88 | 3 | 1 | Laboratory Technician | 2 | Married | 2387 | 6762 | 3 | Y | No | 22 | 4 | 3 | 80 | 1 | 7 | 3 | 3 | 4 | 2 | 0 | 3 |
| 36 | NA | Travel_Frequently | 607 | Sales | 7 | 3 | Marketing | 1 | 1362 | 1 | Female | 83 | 4 | 2 | Sales Executive | 1 | Married | NA | 2261 | 2 | Y | No | 16 | 3 | 4 | 80 | 1 | 17 | 2 | 2 | 15 | 7 | 6 | 13 |
| 48 | No | Travel_Rarely | 855 | Research & Development | 4 | 3 | Life Sciences | 1 | 1363 | 4 | Male | 54 | 3 | 3 | Manufacturing Director | 4 | Single | 7898 | 18706 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 11 | 2 | 3 | 10 | 9 | 0 | 8 |
| 27 | NA | Travel_Rarely | 1291 | Sales | 11 | 3 | Medical | 1 | 1364 | 3 | Female | 98 | 4 | 1 | Sales Representative | 4 | Married | 2534 | 6527 | 8 | Y | No | 14 | 3 | 2 | 80 | 1 | 5 | 4 | 3 | 1 | 0 | 0 | 0 |
| 51 | No | Travel_Rarely | 1405 | Research & Development | 11 | 2 | Technical Degree | 1 | 1367 | 4 | Female | 82 | 2 | 4 | Manufacturing Director | 2 | Single | 13142 | 24439 | 3 | Y | No | 16 | 3 | 2 | 80 | 0 | 29 | 1 | 2 | 5 | 2 | 0 | 3 |
| 18 | No | Non-Travel | 1124 | Research & Development | 1 | 3 | Life Sciences | 1 | 1368 | 4 | Female | 97 | 3 | 1 | Laboratory Technician | 4 | Single | 1611 | 19305 | 1 | Y | No | 15 | 3 | 3 | 80 | 0 | 0 | 5 | 4 | 0 | 0 | 0 | 0 |
| 35 | No | Travel_Rarely | 817 | Research & Development | 1 | 3 | Medical | 1 | 1369 | 4 | Female | 60 | 2 | 2 | Laboratory Technician | 4 | Married | 5363 | 10846 | 0 | Y | No | 12 | 3 | 2 | 80 | 1 | 10 | 0 | 3 | 9 | 7 | 0 | 0 |
| 27 | No | Travel_Frequently | 793 | Sales | 2 | 1 | Life Sciences | 1 | 1371 | 4 | Male | 43 | 1 | 2 | Sales Executive | 4 | Single | 5071 | 20392 | 3 | Y | No | 20 | 4 | 2 | 80 | 0 | 8 | 3 | 3 | 6 | 2 | 0 | 0 |
| 55 | Yes | Travel_Rarely | 267 | Sales | 13 | 4 | Marketing | 1 | 1372 | 1 | Male | 85 | 4 | 4 | Sales Executive | 3 | Single | 13695 | 9277 | 6 | Y | Yes | 17 | 3 | 3 | 80 | 0 | 24 | 2 | 2 | 19 | 7 | 3 | 8 |
| 56 | No | Travel_Rarely | 1369 | Research & Development | 23 | 3 | Life Sciences | 1 | 1373 | 4 | Male | 68 | 3 | 4 | Manufacturing Director | 2 | Married | 13402 | 18235 | 4 | Y | Yes | 12 | 3 | 1 | 80 | 1 | 33 | 0 | 3 | 19 | 16 | 15 | 9 |
| 34 | No | Non-Travel | 999 | Research & Development | 26 | 1 | Technical Degree | 1 | 1374 | 1 | Female | 92 | 2 | 1 | Research Scientist | 3 | Divorced | 2029 | 15891 | 1 | Y | No | 20 | 4 | 3 | 80 | 3 | 5 | 2 | 3 | 5 | 4 | 0 | 0 |
| 40 | No | Travel_Rarely | 1202 | Research & Development | 2 | 1 | Medical | 1 | 1375 | 2 | Female | 89 | 4 | 2 | Healthcare Representative | 3 | Divorced | NA | 13888 | 5 | Y | No | 20 | 4 | 2 | 80 | 3 | 15 | 0 | 3 | 12 | 11 | 11 | 8 |
| 34 | No | Travel_Rarely | 285 | Research & Development | 29 | 3 | Medical | 1 | 1377 | 2 | Male | 86 | 3 | 2 | Laboratory Technician | 3 | Married | 5429 | 17491 | 4 | Y | No | 13 | 3 | 1 | 80 | 2 | 10 | 1 | 3 | 8 | 7 | 7 | 7 |
| 31 | Yes | Travel_Frequently | 703 | Sales | 2 | 3 | Life Sciences | 1 | 1379 | 3 | Female | 90 | 2 | 1 | Sales Representative | 4 | Single | 2785 | 11882 | 7 | Y | No | 14 | 3 | 3 | 80 | 0 | 3 | 3 | 4 | 1 | 0 | 0 | 0 |
| 35 | Yes | Travel_Frequently | 662 | Sales | 18 | 4 | Marketing | 1 | 1380 | 4 | Female | 67 | 3 | 2 | Sales Executive | 3 | Married | 4614 | 23288 | 0 | Y | Yes | 18 | 3 | 3 | 80 | 1 | 5 | 0 | 2 | 4 | 2 | 3 | 2 |
| 38 | No | Travel_Frequently | 693 | Research & Development | 7 | 3 | Life Sciences | 1 | 1382 | 4 | Male | 57 | 4 | 1 | Research Scientist | 3 | Divorced | 2610 | 15748 | 1 | Y | No | 11 | 3 | 4 | 80 | 3 | 4 | 2 | 3 | 4 | 2 | 0 | 3 |
| 34 | No | Travel_Rarely | 404 | Research & Development | 2 | 4 | Technical Degree | 1 | 1383 | 3 | Female | 98 | 3 | 2 | Healthcare Representative | 4 | Single | 6687 | 6163 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 14 | 2 | 4 | 14 | 11 | 4 | 11 |
| 28 | No | Travel_Rarely | 736 | Sales | 26 | 3 | Life Sciences | 1 | 1387 | 3 | Male | 48 | 2 | 2 | Sales Executive | 1 | Married | NA | 24232 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 5 | 0 | 3 | 5 | 3 | 0 | 4 |
| 31 | Yes | Travel_Rarely | 330 | Research & Development | 22 | 4 | Medical | 1 | 1389 | 4 | Male | 98 | 3 | 2 | Manufacturing Director | 3 | Married | 6179 | 21057 | 1 | Y | Yes | 15 | 3 | 4 | 80 | 2 | 10 | 3 | 2 | 10 | 2 | 6 | 7 |
| 39 | No | Travel_Rarely | 1498 | Sales | 21 | 4 | Life Sciences | 1 | 1390 | 1 | Male | 44 | 2 | 2 | Sales Executive | 4 | Married | 6120 | 3567 | 3 | Y | Yes | 12 | 3 | 4 | 80 | 2 | 8 | 2 | 4 | 5 | 4 | 1 | 4 |
| NA | No | Travel_Frequently | 541 | Sales | 2 | 3 | Marketing | 1 | 1391 | 2 | Male | 52 | 3 | 3 | Sales Executive | 2 | Married | 10596 | 15395 | 2 | Y | No | 11 | 3 | 2 | 80 | 0 | 14 | 5 | 3 | 4 | 2 | 3 | 2 |
| 41 | No | Travel_Frequently | 1200 | Research & Development | 22 | 3 | Life Sciences | 1 | 1392 | 4 | Female | 75 | 3 | 2 | Research Scientist | 4 | Divorced | 5467 | 13953 | 3 | Y | Yes | 14 | 3 | 1 | 80 | 2 | 12 | 4 | 2 | 6 | 2 | 3 | 3 |
| 37 | No | Travel_Rarely | 1439 | Research & Development | 4 | 1 | Life Sciences | 1 | 1394 | 3 | Male | 54 | 3 | 1 | Research Scientist | 3 | Married | NA | 5182 | 7 | Y | Yes | 15 | 3 | 4 | 80 | 0 | 8 | 2 | 3 | 6 | 4 | 1 | 3 |
| 33 | No | Travel_Frequently | 1111 | Sales | 5 | 1 | Life Sciences | 1 | 1395 | 2 | Male | 61 | 3 | 2 | Sales Executive | 4 | Married | 9998 | 19293 | 6 | Y | No | 13 | 3 | 1 | 80 | 0 | 8 | 2 | 4 | 5 | 4 | 1 | 2 |
| 32 | No | Travel_Rarely | 499 | Sales | 2 | 1 | Marketing | 1 | 1396 | 3 | Male | 36 | 3 | 2 | Sales Executive | 2 | Married | NA | 20497 | 0 | Y | Yes | 13 | 3 | 1 | 80 | 3 | 4 | 3 | 2 | 3 | 2 | 1 | 2 |
| NA | No | Non-Travel | 1485 | Research & Development | 25 | 2 | Life Sciences | 1 | 1397 | 3 | Male | 71 | 3 | 3 | Healthcare Representative | 3 | Married | 10920 | 3449 | 3 | Y | No | 21 | 4 | 2 | 80 | 1 | 13 | 2 | 3 | 6 | 4 | 0 | 5 |
| NA | No | Travel_Rarely | 1372 | Sales | 18 | 1 | Life Sciences | 1 | 1399 | 1 | Male | 93 | 4 | 2 | Sales Executive | 3 | Married | 6232 | 12477 | 2 | Y | No | 11 | 3 | 2 | 80 | 0 | 6 | 3 | 2 | 3 | 2 | 1 | 2 |
| 52 | No | Travel_Frequently | 322 | Research & Development | 28 | 2 | Medical | 1 | 1401 | 4 | Female | 59 | 4 | 4 | Manufacturing Director | 3 | Married | 13247 | 9731 | 2 | Y | Yes | 11 | 3 | 2 | 80 | 1 | 24 | 3 | 2 | 5 | 3 | 0 | 2 |
| 43 | No | Travel_Rarely | 930 | Research & Development | 6 | 3 | Medical | 1 | 1402 | 1 | Female | 73 | 2 | 2 | Research Scientist | 3 | Single | 4081 | 20003 | 1 | Y | Yes | 14 | 3 | 1 | 80 | 0 | 20 | 3 | 1 | 20 | 7 | 1 | 8 |
| 27 | NA | Travel_Rarely | 205 | Sales | 10 | 3 | Marketing | 1 | 1403 | 4 | Female | 98 | 2 | 2 | Sales Executive | 4 | Married | 5769 | 7100 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 6 | 3 | 3 | 6 | 2 | 4 | 4 |
| 27 | Yes | Travel_Rarely | 135 | Research & Development | 17 | 4 | Life Sciences | 1 | 1405 | 4 | Female | 51 | 3 | 1 | Research Scientist | 3 | Single | NA | 25681 | 1 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 8 | 2 | 3 | 8 | 2 | 7 | 7 |
| 26 | No | Travel_Rarely | 683 | Research & Development | 2 | 1 | Medical | 1 | 1407 | 1 | Male | 36 | 2 | 1 | Research Scientist | 4 | Single | 3904 | 4050 | 0 | Y | No | 12 | 3 | 4 | 80 | 0 | 5 | 2 | 3 | 4 | 3 | 1 | 1 |
| 42 | No | Travel_Rarely | 1147 | Human Resources | 10 | 3 | Human Resources | 1 | 1408 | 3 | Female | 31 | 3 | 4 | Manager | 1 | Married | 16799 | 16616 | 0 | Y | No | 14 | 3 | 3 | 80 | 1 | 21 | 5 | 3 | 20 | 7 | 0 | 9 |
| 52 | No | Travel_Rarely | 258 | Research & Development | 8 | 4 | Other | 1 | 1409 | 3 | Female | 54 | 3 | 1 | Laboratory Technician | 1 | Married | 2950 | 17363 | 9 | Y | No | 13 | 3 | 3 | 80 | 0 | 12 | 2 | 1 | 5 | 4 | 0 | 4 |
| 37 | No | Travel_Rarely | 1462 | Research & Development | 11 | 3 | Medical | 1 | 1411 | 1 | Female | 94 | 3 | 1 | Laboratory Technician | 3 | Single | 3629 | 19106 | 4 | Y | No | 18 | 3 | 1 | 80 | 0 | 8 | 6 | 3 | 3 | 2 | 0 | 2 |
| 35 | No | Travel_Frequently | 200 | Research & Development | 18 | 2 | Life Sciences | 1 | 1412 | 3 | Male | 60 | 3 | 3 | Manufacturing Director | 4 | Single | 9362 | 19944 | 2 | Y | No | 11 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 2 | 2 | 2 | 2 |
| NA | No | Travel_Rarely | 949 | Research & Development | 1 | 3 | Technical Degree | 1 | 1415 | 1 | Male | 81 | 3 | 1 | Laboratory Technician | 4 | Married | 3229 | 4910 | 4 | Y | No | 11 | 3 | 2 | 80 | 1 | 7 | 2 | 2 | 3 | 2 | 0 | 2 |
| 26 | No | Travel_Rarely | 652 | Research & Development | 7 | 3 | Other | 1 | 1417 | 3 | Male | 100 | 4 | 1 | Laboratory Technician | 1 | Single | 3578 | 23577 | 0 | Y | No | 12 | 3 | 4 | 80 | 0 | 8 | 2 | 3 | 7 | 7 | 0 | 7 |
| 29 | No | Travel_Rarely | 332 | Human Resources | 17 | 3 | Other | 1 | 1419 | 2 | Male | 51 | 2 | 3 | Human Resources | 1 | Single | 7988 | 9769 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 10 | 3 | 2 | 10 | 9 | 0 | 9 |
| 49 | Yes | Travel_Frequently | 1475 | Research & Development | 28 | 2 | Life Sciences | 1 | 1420 | 1 | Male | 97 | 2 | 2 | Laboratory Technician | 1 | Single | 4284 | 22710 | 3 | Y | No | 20 | 4 | 1 | 80 | 0 | 20 | 2 | 3 | 4 | 3 | 1 | 3 |
| 29 | Yes | Travel_Frequently | 337 | Research & Development | 14 | 1 | Other | 1 | 1421 | 3 | Female | 84 | 3 | 3 | Healthcare Representative | 4 | Single | 7553 | 22930 | 0 | Y | Yes | 12 | 3 | 1 | 80 | 0 | 9 | 1 | 3 | 8 | 7 | 7 | 7 |
| 54 | No | Travel_Rarely | 971 | Research & Development | 1 | 3 | Medical | 1 | 1422 | 4 | Female | 54 | 3 | 4 | Research Director | 4 | Single | 17328 | 5652 | 6 | Y | No | 19 | 3 | 4 | 80 | 0 | 29 | 3 | 2 | 20 | 7 | 12 | 7 |
| 58 | No | Travel_Rarely | 1055 | Research & Development | 1 | 3 | Medical | 1 | 1423 | 4 | Female | 76 | 3 | 5 | Research Director | 1 | Married | 19701 | 22456 | 3 | Y | Yes | 21 | 4 | 3 | 80 | 1 | 32 | 3 | 3 | 9 | 8 | 1 | 5 |
| 55 | No | Travel_Rarely | 1136 | Research & Development | 1 | 4 | Medical | 1 | 1424 | 2 | Male | 81 | 4 | 4 | Research Director | 4 | Divorced | 14732 | 12414 | 2 | Y | No | 13 | 3 | 4 | 80 | 2 | 31 | 4 | 4 | 7 | 7 | 0 | 0 |
| 36 | No | Travel_Rarely | 1174 | Sales | 3 | 4 | Marketing | 1 | 1425 | 1 | Female | 99 | 3 | 2 | Sales Executive | 2 | Single | 9278 | 20763 | 3 | Y | Yes | 16 | 3 | 4 | 80 | 0 | 15 | 3 | 3 | 5 | 4 | 0 | 1 |
| 31 | Yes | Travel_Frequently | 667 | Sales | 1 | 4 | Life Sciences | 1 | 1427 | 2 | Female | 50 | 1 | 1 | Sales Representative | 3 | Single | 1359 | 16154 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 30 | No | Travel_Rarely | 855 | Sales | 7 | 4 | Marketing | 1 | 1428 | 4 | Female | 73 | 3 | 2 | Sales Executive | 1 | Divorced | 4779 | 12761 | 7 | Y | No | 14 | 3 | 2 | 80 | 2 | 8 | 3 | 3 | 3 | 2 | 0 | 2 |
| 31 | No | Travel_Rarely | 182 | Research & Development | 8 | 5 | Life Sciences | 1 | 1430 | 1 | Female | 93 | 3 | 4 | Research Director | 2 | Single | 16422 | 8847 | 3 | Y | No | 11 | 3 | 3 | 80 | 0 | 9 | 3 | 4 | 3 | 2 | 1 | 0 |
| 34 | No | Travel_Frequently | 560 | Research & Development | 1 | 4 | Other | 1 | 1431 | 4 | Male | 91 | 3 | 1 | Research Scientist | 1 | Divorced | 2996 | 20284 | 5 | Y | No | 14 | 3 | 3 | 80 | 2 | 10 | 2 | 3 | 4 | 3 | 1 | 3 |
| 31 | NA | Travel_Rarely | 202 | Research & Development | 8 | 3 | Life Sciences | 1 | 1433 | 1 | Female | 34 | 2 | 1 | Research Scientist | 2 | Single | 1261 | 22262 | 1 | Y | No | 12 | 3 | 3 | 80 | 0 | 1 | 3 | 4 | 1 | 0 | 0 | 0 |
| 27 | No | Travel_Rarely | 1377 | Research & Development | 11 | 1 | Life Sciences | 1 | 1434 | 2 | Male | 91 | 3 | 1 | Laboratory Technician | 1 | Married | 2099 | 7679 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 6 | 3 | 4 | 5 | 0 | 1 | 4 |
| 36 | No | Travel_Rarely | 172 | Research & Development | 4 | 4 | Life Sciences | 1 | 1435 | 1 | Male | 37 | 2 | 2 | Laboratory Technician | 4 | Single | 5810 | 22604 | 1 | Y | No | 16 | 3 | 3 | 80 | 0 | 10 | 2 | 2 | 10 | 4 | 1 | 8 |
| 36 | NA | Travel_Rarely | 329 | Sales | 16 | 4 | Marketing | 1 | 1436 | 3 | Female | 98 | 2 | 2 | Sales Executive | 1 | Married | 5647 | 13494 | 4 | Y | No | 13 | 3 | 1 | 80 | 2 | 11 | 3 | 2 | 3 | 2 | 0 | 2 |
| 47 | No | Travel_Rarely | 465 | Research & Development | 1 | 3 | Technical Degree | 1 | 1438 | 1 | Male | 74 | 3 | 1 | Research Scientist | 4 | Married | 3420 | 10205 | 7 | Y | No | 12 | 3 | 3 | 80 | 1 | 17 | 2 | 2 | 6 | 5 | 1 | 2 |
| 25 | Yes | Travel_Rarely | 383 | Sales | 9 | 2 | Life Sciences | 1 | 1439 | 1 | Male | 68 | 2 | 1 | Sales Representative | 1 | Married | 4400 | 15182 | 3 | Y | No | 12 | 3 | 1 | 80 | 0 | 6 | 2 | 3 | 3 | 2 | 2 | 2 |
| 37 | No | Non-Travel | 1413 | Research & Development | 5 | 2 | Technical Degree | 1 | 1440 | 3 | Male | 84 | 4 | 1 | Laboratory Technician | 3 | Single | 3500 | 25470 | 0 | Y | No | 14 | 3 | 1 | 80 | 0 | 7 | 2 | 1 | 6 | 5 | 1 | 3 |
| NA | No | Travel_Rarely | 1255 | Research & Development | 1 | 2 | Life Sciences | 1 | 1441 | 1 | Female | 90 | 3 | 1 | Research Scientist | 1 | Married | 2066 | 10494 | 2 | Y | No | 22 | 4 | 4 | 80 | 1 | 5 | 3 | 4 | 3 | 2 | 1 | 0 |
| 47 | No | Travel_Rarely | 359 | Research & Development | 2 | 4 | Medical | 1 | 1443 | 1 | Female | 82 | 3 | 4 | Research Director | 3 | Married | 17169 | 26703 | 3 | Y | No | 19 | 3 | 2 | 80 | 2 | 26 | 2 | 4 | 20 | 17 | 5 | 6 |
| 24 | No | Travel_Rarely | 1476 | Sales | 4 | 1 | Medical | 1 | 1445 | 4 | Female | 42 | 3 | 2 | Sales Executive | 3 | Married | 4162 | 15211 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 2 | 5 | 3 | 3 | 5 | 4 | 0 | 3 |
| 32 | NA | Travel_Rarely | 601 | Sales | 7 | 5 | Marketing | 1 | 1446 | 4 | Male | 97 | 3 | 2 | Sales Executive | 4 | Married | 9204 | 23343 | 4 | Y | No | 12 | 3 | 3 | 80 | 1 | 7 | 3 | 2 | 4 | 3 | 0 | 3 |
| 34 | No | Travel_Rarely | 401 | Research & Development | 1 | 3 | Life Sciences | 1 | 1447 | 4 | Female | 86 | 2 | 1 | Laboratory Technician | 2 | Married | 3294 | 3708 | 5 | Y | No | 17 | 3 | 1 | 80 | 1 | 7 | 2 | 2 | 5 | 4 | 0 | 2 |
| 41 | No | Travel_Rarely | 1283 | Research & Development | 5 | 5 | Medical | 1 | 1448 | 2 | Male | 90 | 4 | 1 | Research Scientist | 3 | Married | 2127 | 5561 | 2 | Y | Yes | 12 | 3 | 1 | 80 | 0 | 7 | 5 | 2 | 4 | 2 | 0 | 3 |
| 40 | No | Non-Travel | 663 | Research & Development | 9 | 4 | Other | 1 | 1449 | 3 | Male | 81 | 3 | 2 | Laboratory Technician | 3 | Divorced | 3975 | 23099 | 3 | Y | No | 11 | 3 | 3 | 80 | 2 | 11 | 2 | 4 | 8 | 7 | 0 | 7 |
| 31 | No | Travel_Rarely | 326 | Sales | 8 | 2 | Life Sciences | 1 | 1453 | 1 | Male | 31 | 3 | 3 | Sales Executive | 4 | Divorced | 10793 | 8386 | 1 | Y | No | 18 | 3 | 1 | 80 | 1 | 13 | 5 | 3 | 13 | 7 | 9 | 9 |
| 46 | Yes | Travel_Rarely | 377 | Sales | 9 | 3 | Marketing | 1 | 1457 | 1 | Male | 52 | 3 | 3 | Sales Executive | 4 | Divorced | 10096 | 15986 | 4 | Y | No | 11 | 3 | 1 | 80 | 1 | 28 | 1 | 4 | 7 | 7 | 4 | 3 |
| 39 | Yes | Non-Travel | 592 | Research & Development | 2 | 3 | Life Sciences | 1 | 1458 | 1 | Female | 54 | 2 | 1 | Laboratory Technician | 1 | Single | NA | 17181 | 2 | Y | Yes | 23 | 4 | 2 | 80 | 0 | 11 | 2 | 4 | 1 | 0 | 0 | 0 |
| 31 | Yes | Travel_Frequently | 1445 | Research & Development | 1 | 5 | Life Sciences | 1 | 1459 | 3 | Female | 100 | 4 | 3 | Manufacturing Director | 2 | Single | 7446 | 8931 | 1 | Y | No | 11 | 3 | 1 | 80 | 0 | 10 | 2 | 3 | 10 | 8 | 4 | 7 |
| 45 | No | Travel_Rarely | 1038 | Research & Development | 20 | 3 | Medical | 1 | 1460 | 2 | Male | 95 | 1 | 3 | Healthcare Representative | 1 | Divorced | 10851 | 19863 | 2 | Y | Yes | 18 | 3 | 2 | 80 | 1 | 24 | 2 | 3 | 7 | 7 | 0 | 7 |
| 31 | No | Travel_Rarely | 1398 | Human Resources | 8 | 2 | Medical | 1 | 1461 | 4 | Female | 96 | 4 | 1 | Human Resources | 2 | Single | 2109 | 24609 | 9 | Y | No | 18 | 3 | 4 | 80 | 0 | 8 | 3 | 3 | 3 | 2 | 0 | 2 |
| 31 | Yes | Travel_Frequently | 523 | Research & Development | 2 | 3 | Life Sciences | 1 | 1464 | 2 | Male | 94 | 3 | 1 | Laboratory Technician | 4 | Married | 3722 | 21081 | 6 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 7 | 2 | 1 | 2 | 2 | 2 | 2 |
| 45 | No | Travel_Rarely | 1448 | Research & Development | 29 | 3 | Technical Degree | 1 | 1465 | 2 | Male | 55 | 3 | 3 | Manufacturing Director | 4 | Married | 9380 | 14720 | 4 | Y | Yes | 18 | 3 | 4 | 80 | 2 | 10 | 4 | 4 | 3 | 1 | 1 | 2 |
| 48 | No | Travel_Rarely | 1221 | Sales | 7 | 3 | Marketing | 1 | 1466 | 3 | Male | 96 | 3 | 2 | Sales Executive | 1 | Divorced | 5486 | 24795 | 4 | Y | No | 11 | 3 | 1 | 80 | 3 | 15 | 3 | 3 | 2 | 2 | 2 | 2 |
| 34 | Yes | Travel_Rarely | 1107 | Human Resources | 9 | 4 | Technical Degree | 1 | 1467 | 1 | Female | 52 | 3 | 1 | Human Resources | 3 | Married | 2742 | 3072 | 1 | Y | No | 15 | 3 | 4 | 80 | 0 | 2 | 0 | 3 | 2 | 2 | 2 | 2 |
| 40 | NA | Non-Travel | 218 | Research & Development | 8 | 1 | Medical | 1 | 1468 | 4 | Male | 55 | 2 | 3 | Research Director | 2 | Divorced | 13757 | 25178 | 2 | Y | No | 11 | 3 | 3 | 80 | 1 | 16 | 5 | 3 | 9 | 8 | 4 | 8 |
| 28 | No | Travel_Rarely | 866 | Sales | 5 | 3 | Medical | 1 | 1469 | 4 | Male | 84 | 3 | 2 | Sales Executive | 1 | Single | 8463 | 23490 | 0 | Y | No | 18 | 3 | 4 | 80 | 0 | 6 | 4 | 3 | 5 | 4 | 1 | 3 |
| 44 | No | Non-Travel | 981 | Research & Development | 5 | 3 | Life Sciences | 1 | 1471 | 3 | Male | 90 | 2 | 1 | Laboratory Technician | 3 | Single | 3162 | 7973 | 3 | Y | No | 14 | 3 | 4 | 80 | 0 | 7 | 5 | 3 | 5 | 2 | 0 | 3 |
| 53 | No | Travel_Rarely | 447 | Research & Development | 2 | 3 | Medical | 1 | 1472 | 4 | Male | 39 | 4 | 4 | Research Director | 2 | Single | 16598 | 19764 | 4 | Y | No | 12 | 3 | 2 | 80 | 0 | 35 | 2 | 2 | 9 | 8 | 8 | 8 |
| 49 | NA | Travel_Rarely | 1495 | Research & Development | 5 | 4 | Technical Degree | 1 | 1473 | 1 | Male | 96 | 3 | 2 | Healthcare Representative | 3 | Married | 6651 | 21534 | 2 | Y | No | 14 | 3 | 2 | 80 | 1 | 20 | 0 | 2 | 3 | 2 | 1 | 2 |
| 40 | NA | Travel_Rarely | 896 | Research & Development | 2 | 3 | Medical | 1 | 1474 | 3 | Male | 68 | 3 | 1 | Research Scientist | 3 | Divorced | 2345 | 8045 | 2 | Y | No | 14 | 3 | 3 | 80 | 1 | 8 | 3 | 4 | 3 | 1 | 1 | 2 |
| 44 | No | Travel_Rarely | 1467 | Research & Development | 20 | 3 | Life Sciences | 1 | 1475 | 4 | Male | 49 | 3 | 1 | Research Scientist | 2 | Single | 3420 | 21158 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 6 | 3 | 2 | 5 | 2 | 1 | 3 |
| 33 | No | Travel_Frequently | 430 | Sales | 7 | 3 | Medical | 1 | 1477 | 4 | Male | 54 | 3 | 2 | Sales Executive | 1 | Married | 4373 | 17456 | 0 | Y | No | 14 | 3 | 1 | 80 | 2 | 5 | 2 | 3 | 4 | 3 | 0 | 3 |
| 34 | No | Travel_Rarely | 1326 | Sales | 3 | 3 | Other | 1 | 1478 | 4 | Male | 81 | 1 | 2 | Sales Executive | 1 | Single | 4759 | 15891 | 3 | Y | No | 18 | 3 | 4 | 80 | 0 | 15 | 2 | 3 | 13 | 9 | 3 | 12 |
| 30 | No | Travel_Rarely | 1358 | Sales | 16 | 1 | Life Sciences | 1 | 1479 | 4 | Male | 96 | 3 | 2 | Sales Executive | 3 | Married | 5301 | 2939 | 8 | Y | No | 15 | 3 | 3 | 80 | 2 | 4 | 2 | 2 | 2 | 1 | 2 | 2 |
| 42 | No | Travel_Frequently | 748 | Research & Development | 9 | 2 | Medical | 1 | 1480 | 1 | Female | 74 | 3 | 1 | Laboratory Technician | 4 | Single | 3673 | 16458 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 12 | 3 | 3 | 12 | 9 | 5 | 8 |
| 44 | No | Travel_Frequently | 383 | Sales | 1 | 5 | Marketing | 1 | 1481 | 1 | Female | 79 | 3 | 2 | Sales Executive | 3 | Married | 4768 | 9282 | 7 | Y | No | 12 | 3 | 3 | 80 | 1 | 11 | 4 | 2 | 1 | 0 | 0 | 0 |
| 30 | No | Non-Travel | 990 | Research & Development | 7 | 3 | Technical Degree | 1 | 1482 | 3 | Male | 64 | 3 | 1 | Research Scientist | 3 | Divorced | 1274 | 7152 | 1 | Y | No | 13 | 3 | 2 | 80 | 2 | 1 | 2 | 2 | 1 | 0 | 0 | 0 |
| 57 | No | Travel_Rarely | 405 | Research & Development | 1 | 2 | Life Sciences | 1 | 1483 | 2 | Male | 93 | 4 | 2 | Research Scientist | 3 | Married | 4900 | 2721 | 0 | Y | No | 24 | 4 | 1 | 80 | 1 | 13 | 2 | 2 | 12 | 9 | 2 | 8 |
| 49 | No | Travel_Rarely | 1490 | Research & Development | 7 | 4 | Life Sciences | 1 | 1484 | 3 | Male | 35 | 3 | 3 | Healthcare Representative | 2 | Divorced | 10466 | 20948 | 3 | Y | No | 14 | 3 | 2 | 80 | 2 | 29 | 3 | 3 | 8 | 7 | 0 | 7 |
| 34 | No | Travel_Frequently | 829 | Research & Development | 15 | 3 | Medical | 1 | 1485 | 2 | Male | 71 | 3 | 4 | Research Director | 1 | Divorced | 17007 | 11929 | 7 | Y | No | 14 | 3 | 4 | 80 | 2 | 16 | 3 | 2 | 14 | 8 | 6 | 9 |
| NA | Yes | Travel_Frequently | 1496 | Sales | 1 | 3 | Technical Degree | 1 | 1486 | 1 | Male | 92 | 3 | 1 | Sales Representative | 3 | Married | 2909 | 15747 | 3 | Y | No | 15 | 3 | 4 | 80 | 1 | 5 | 3 | 4 | 3 | 2 | 1 | 2 |
| 29 | Yes | Travel_Frequently | 115 | Sales | 13 | 3 | Technical Degree | 1 | 1487 | 1 | Female | 51 | 3 | 2 | Sales Executive | 2 | Single | 5765 | 17485 | 5 | Y | No | 11 | 3 | 1 | 80 | 0 | 7 | 4 | 1 | 5 | 3 | 0 | 0 |
| 34 | Yes | Travel_Rarely | 790 | Sales | 24 | 4 | Medical | 1 | 1489 | 1 | Female | 40 | 2 | 2 | Sales Executive | 2 | Single | 4599 | 7815 | 0 | Y | Yes | 23 | 4 | 3 | 80 | 0 | 16 | 2 | 4 | 15 | 9 | 10 | 10 |
| 35 | No | 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 | 0 | 0 | 0 |
| 24 | Yes | Travel_Frequently | 381 | Research & Development | 9 | 3 | Medical | 1 | 1494 | 2 | Male | 89 | 3 | 1 | Laboratory Technician | 1 | Single | NA | 16998 | 2 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 4 | 2 | 2 | 0 | 0 | 0 | 0 |
| 24 | No | Non-Travel | 830 | Sales | 13 | 2 | Life Sciences | 1 | 1495 | 4 | Female | 78 | 3 | 1 | Sales Representative | 2 | Married | NA | 7103 | 1 | Y | No | 13 | 3 | 3 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 44 | No | 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 | 0 | 16 | 2 | 2 | 2 | 2 | 2 | 2 |
| 29 | No | 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 | 0 | 4 |
| NA | No | Travel_Rarely | 330 | Human Resources | 1 | 3 | Life Sciences | 1 | 1499 | 3 | Male | 46 | 3 | 1 | Human Resources | 3 | Divorced | NA | 15428 | 0 | Y | No | 21 | 4 | 1 | 80 | 1 | 6 | 3 | 4 | 5 | 3 | 1 | 3 |
| 55 | No | Travel_Rarely | 1229 | Research & Development | 4 | 4 | Life Sciences | 1 | 1501 | 4 | Male | 30 | 3 | 2 | Healthcare Representative | 3 | Married | 4035 | 16143 | 0 | Y | Yes | 16 | 3 | 2 | 80 | 0 | 4 | 2 | 3 | 3 | 2 | 1 | 2 |
| 33 | No | 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 | 0 | 8 | 5 | 3 | 5 | 4 | 0 | 2 |
| 47 | No | 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 |
| 28 | Yes | Travel_Frequently | 289 | Research & Development | 2 | 2 | Medical | 1 | 1504 | 3 | Male | 38 | 2 | 1 | Laboratory Technician | 1 | Single | 2561 | 5355 | 7 | Y | No | 11 | 3 | 3 | 80 | 0 | 8 | 2 | 2 | 0 | 0 | 0 | 0 |
| 28 | No | Travel_Rarely | 1423 | Research & Development | 1 | 3 | Life Sciences | 1 | 1506 | 1 | Male | 72 | 2 | 1 | Research Scientist | 3 | Divorced | 1563 | 12530 | 1 | Y | No | 14 | 3 | 4 | 80 | 1 | 1 | 2 | 1 | 1 | 0 | 0 | 0 |
| 28 | No | Travel_Frequently | 467 | Sales | 7 | 3 | Life Sciences | 1 | 1507 | 3 | Male | 55 | 3 | 2 | Sales Executive | 1 | Single | 4898 | 11827 | 0 | Y | No | 14 | 3 | 4 | 80 | 0 | 5 | 5 | 3 | 4 | 2 | 1 | 3 |
| 49 | NA | Travel_Rarely | 271 | Research & Development | 3 | 2 | Medical | 1 | 1509 | 3 | Female | 43 | 2 | 2 | Laboratory Technician | 1 | Married | 4789 | 23070 | 4 | Y | No | 25 | 4 | 1 | 80 | 1 | 10 | 3 | 3 | 3 | 2 | 1 | 2 |
| 29 | NA | Travel_Frequently | 410 | Research & Development | 2 | 1 | Life Sciences | 1 | 1513 | 4 | Female | 97 | 3 | 1 | Laboratory Technician | 2 | Married | 3180 | 4668 | 0 | Y | No | 13 | 3 | 3 | 80 | 3 | 4 | 3 | 3 | 3 | 2 | 0 | 2 |
| 28 | No | Travel_Rarely | 1083 | Research & Development | 29 | 1 | Life Sciences | 1 | 1514 | 3 | Male | 96 | 1 | 2 | Manufacturing Director | 2 | Married | 6549 | 3173 | 1 | Y | No | 14 | 3 | 2 | 80 | 2 | 8 | 2 | 2 | 8 | 6 | 1 | 7 |
| NA | No | Travel_Rarely | 516 | Research & Development | 8 | 5 | Life Sciences | 1 | 1515 | 4 | Male | 69 | 3 | 2 | Healthcare Representative | 3 | Single | 6388 | 22049 | 2 | Y | Yes | 17 | 3 | 1 | 80 | 0 | 14 | 6 | 3 | 0 | 0 | 0 | 0 |
| 32 | NA | Travel_Rarely | 495 | Research & Development | 10 | 3 | Medical | 1 | 1516 | 3 | Male | 64 | 3 | 3 | Manager | 4 | Single | 11244 | 21072 | 2 | Y | No | 25 | 4 | 2 | 80 | 0 | 10 | 5 | 4 | 5 | 2 | 0 | 0 |
| 54 | No | Travel_Frequently | 1050 | Research & Development | 11 | 4 | Medical | 1 | 1520 | 2 | Female | 87 | 3 | 4 | Manager | 4 | Divorced | NA | 24456 | 3 | Y | No | 20 | 4 | 1 | 80 | 1 | 26 | 2 | 3 | 14 | 9 | 1 | 12 |
| 29 | Yes | Travel_Rarely | 224 | Research & Development | 1 | 4 | Technical Degree | 1 | 1522 | 1 | Male | 100 | 2 | 1 | Research Scientist | 1 | Single | 2362 | 7568 | 6 | Y | No | 13 | 3 | 3 | 80 | 0 | 11 | 2 | 1 | 9 | 7 | 0 | 7 |
| 44 | No | Travel_Rarely | 136 | Research & Development | 28 | 3 | Life Sciences | 1 | 1523 | 4 | Male | 32 | 3 | 4 | Research Director | 1 | Married | 16328 | 22074 | 3 | Y | No | 13 | 3 | 3 | 80 | 1 | 24 | 1 | 4 | 20 | 6 | 14 | 17 |
| 39 | No | Travel_Rarely | 1089 | Research & Development | 6 | 3 | Life Sciences | 1 | 1525 | 2 | Female | 32 | 3 | 3 | Manufacturing Director | 2 | Single | 8376 | 9150 | 4 | Y | No | 18 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 2 | 0 | 2 | 2 |
| 46 | No | Travel_Rarely | 228 | Sales | 3 | 3 | Life Sciences | 1 | 1527 | 3 | Female | 51 | 3 | 4 | Manager | 2 | Married | 16606 | 11380 | 8 | Y | No | 12 | 3 | 4 | 80 | 1 | 23 | 2 | 4 | 13 | 12 | 5 | 1 |
| 35 | No | Travel_Rarely | 1029 | Research & Development | 16 | 3 | Life Sciences | 1 | 1529 | 4 | Female | 91 | 2 | 3 | Healthcare Representative | 2 | Single | 8606 | 21195 | 1 | Y | No | 19 | 3 | 4 | 80 | 0 | 11 | 3 | 1 | 11 | 8 | 3 | 3 |
| 23 | No | Travel_Rarely | 507 | Research & Development | 20 | 1 | Life Sciences | 1 | 1533 | 1 | Male | 97 | 3 | 2 | Laboratory Technician | 3 | Single | 2272 | 24812 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 5 | 2 | 3 | 4 | 3 | 1 | 2 |
| 40 | Yes | Travel_Rarely | 676 | Research & Development | 9 | 4 | Life Sciences | 1 | 1534 | 4 | Male | 86 | 3 | 1 | Laboratory Technician | 1 | Single | 2018 | 21831 | 3 | Y | No | 14 | 3 | 2 | 80 | 0 | 15 | 3 | 1 | 5 | 4 | 1 | 0 |
| 34 | No | Travel_Rarely | 971 | Sales | 1 | 3 | Technical Degree | 1 | 1535 | 4 | Male | 64 | 2 | 3 | Sales Executive | 3 | Married | 7083 | 12288 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 0 | 10 | 3 | 3 | 10 | 9 | 8 | 6 |
| 31 | Yes | Travel_Frequently | 561 | Research & Development | 3 | 3 | Life Sciences | 1 | 1537 | 4 | Female | 33 | 3 | 1 | Research Scientist | 3 | Single | 4084 | 4156 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 7 | 2 | 1 | 7 | 2 | 7 | 7 |
| 50 | No | Travel_Frequently | 333 | Research & Development | 22 | 5 | Medical | 1 | 1539 | 3 | Male | 88 | 1 | 4 | Research Director | 4 | Single | NA | 24450 | 1 | Y | Yes | 13 | 3 | 4 | 80 | 0 | 32 | 2 | 3 | 32 | 6 | 13 | 9 |
| NA | No | Travel_Rarely | 1440 | Sales | 7 | 2 | Technical Degree | 1 | 1541 | 2 | Male | 55 | 3 | 1 | Sales Representative | 3 | Married | 2308 | 4944 | 0 | Y | Yes | 25 | 4 | 2 | 80 | 1 | 12 | 4 | 3 | 11 | 10 | 5 | 7 |
| 42 | NA | Travel_Rarely | 1210 | Research & Development | 2 | 3 | Medical | 1 | 1542 | 3 | Male | 68 | 2 | 1 | Laboratory Technician | 2 | Married | NA | 24052 | 4 | Y | No | 14 | 3 | 2 | 80 | 1 | 4 | 3 | 3 | 1 | 0 | 0 | 0 |
| 37 | No | Travel_Rarely | 674 | Research & Development | 13 | 3 | Medical | 1 | 1543 | 1 | Male | 47 | 3 | 2 | Research Scientist | 4 | Married | 4285 | 3031 | 1 | Y | No | 17 | 3 | 1 | 80 | 0 | 10 | 2 | 3 | 10 | 8 | 3 | 7 |
| 29 | No | Travel_Rarely | 441 | Research & Development | 8 | 1 | Other | 1 | 1544 | 3 | Female | 39 | 1 | 2 | Healthcare Representative | 1 | Married | 9715 | 7288 | 3 | Y | No | 13 | 3 | 3 | 80 | 1 | 9 | 3 | 3 | 7 | 7 | 0 | 7 |
| 33 | No | Travel_Rarely | 575 | Research & Development | 25 | 3 | Life Sciences | 1 | 1545 | 4 | Male | 44 | 2 | 2 | Manufacturing Director | 2 | Single | 4320 | 24152 | 1 | Y | No | 13 | 3 | 4 | 80 | 0 | 5 | 2 | 3 | 5 | 3 | 0 | 2 |
| 45 | No | Travel_Rarely | 950 | Research & Development | 28 | 3 | Technical Degree | 1 | 1546 | 4 | Male | 97 | 3 | 1 | Research Scientist | 4 | Married | 2132 | 4585 | 4 | Y | No | 20 | 4 | 4 | 80 | 1 | 8 | 3 | 3 | 5 | 4 | 0 | 3 |
| NA | No | Travel_Frequently | 288 | Research & Development | 2 | 3 | Life Sciences | 1 | 1547 | 4 | Male | 40 | 3 | 3 | Healthcare Representative | 4 | Married | 10124 | 18611 | 2 | Y | Yes | 14 | 3 | 3 | 80 | 1 | 24 | 3 | 1 | 20 | 8 | 13 | 9 |
| 40 | No | Travel_Rarely | 1342 | Sales | 9 | 2 | Medical | 1 | 1548 | 1 | Male | 47 | 3 | 2 | Sales Executive | 1 | Married | 5473 | 19345 | 0 | Y | No | 12 | 3 | 4 | 80 | 0 | 9 | 5 | 4 | 8 | 4 | 7 | 1 |
| 33 | NA | Travel_Rarely | 589 | Research & Development | 28 | 4 | Life Sciences | 1 | 1549 | 2 | Male | 79 | 3 | 2 | Laboratory Technician | 3 | Married | 5207 | 22949 | 1 | Y | Yes | 12 | 3 | 2 | 80 | 1 | 15 | 3 | 3 | 15 | 14 | 5 | 7 |
| 40 | No | Travel_Rarely | 898 | Human Resources | 6 | 2 | Medical | 1 | 1550 | 3 | Male | 38 | 3 | 4 | Manager | 4 | Single | 16437 | 17381 | 1 | Y | Yes | 21 | 4 | 4 | 80 | 0 | 21 | 2 | 3 | 21 | 7 | 7 | 7 |
| 24 | No | Travel_Rarely | 350 | Research & Development | 21 | 2 | Technical Degree | 1 | 1551 | 3 | Male | 57 | 2 | 1 | Laboratory Technician | 1 | Divorced | 2296 | 10036 | 0 | Y | No | 14 | 3 | 2 | 80 | 3 | 2 | 3 | 3 | 1 | 1 | 0 | 0 |
| 40 | No | Non-Travel | 1142 | Research & Development | 8 | 2 | Life Sciences | 1 | 1552 | 4 | Male | 72 | 3 | 2 | Healthcare Representative | 4 | Divorced | 4069 | 8841 | 3 | Y | Yes | 18 | 3 | 3 | 80 | 0 | 8 | 2 | 3 | 2 | 2 | 2 | 2 |
| 45 | NA | Travel_Rarely | 538 | Research & Development | 1 | 4 | Technical Degree | 1 | 1553 | 1 | Male | 66 | 3 | 3 | Healthcare Representative | 2 | Divorced | 7441 | 20933 | 1 | Y | No | 12 | 3 | 1 | 80 | 3 | 10 | 4 | 3 | 10 | 8 | 7 | 7 |
| 35 | NA | Travel_Rarely | 1402 | Sales | 28 | 4 | Life Sciences | 1 | 1554 | 2 | Female | 98 | 2 | 1 | Sales Representative | 3 | Married | NA | 26204 | 0 | Y | No | 23 | 4 | 1 | 80 | 2 | 6 | 5 | 3 | 5 | 3 | 4 | 2 |
| 32 | NA | Travel_Rarely | 824 | Research & Development | 5 | 2 | Life Sciences | 1 | 1555 | 4 | Female | 67 | 2 | 2 | Research Scientist | 2 | Married | 5878 | 15624 | 3 | Y | No | 12 | 3 | 1 | 80 | 1 | 12 | 2 | 3 | 7 | 1 | 2 | 5 |
| 36 | No | Travel_Rarely | 1157 | Sales | 2 | 4 | Life Sciences | 1 | 1556 | 3 | Male | 70 | 3 | 1 | Sales Representative | 4 | Single | 2644 | 17001 | 3 | Y | Yes | 21 | 4 | 4 | 80 | 0 | 7 | 3 | 2 | 3 | 2 | 1 | 2 |
| 48 | No | Travel_Rarely | 492 | Sales | 16 | 4 | Life Sciences | 1 | 1557 | 3 | Female | 96 | 3 | 2 | Sales Executive | 3 | Divorced | 6439 | 13693 | 8 | Y | No | 14 | 3 | 3 | 80 | 1 | 18 | 2 | 3 | 8 | 7 | 7 | 7 |
| 29 | No | Travel_Rarely | 598 | Research & Development | 9 | 3 | Life Sciences | 1 | 1558 | 3 | Male | 91 | 4 | 1 | Research Scientist | 3 | Married | 2451 | 22376 | 6 | Y | No | 18 | 3 | 1 | 80 | 2 | 5 | 2 | 2 | 1 | 0 | 0 | 0 |
| 33 | No | Travel_Rarely | 1242 | Sales | 8 | 4 | Life Sciences | 1 | 1560 | 1 | Male | 46 | 3 | 2 | Sales Executive | 1 | Married | NA | 10589 | 2 | Y | No | 13 | 3 | 4 | 80 | 1 | 8 | 6 | 1 | 2 | 2 | 2 | 2 |
| 30 | Yes | Travel_Rarely | 740 | Sales | 1 | 3 | Life Sciences | 1 | 1562 | 2 | Male | 64 | 2 | 2 | Sales Executive | 1 | Married | 9714 | 5323 | 1 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 4 | 3 | 10 | 8 | 6 | 7 |
| 38 | No | Travel_Frequently | 888 | Human Resources | 10 | 4 | Human Resources | 1 | 1563 | 3 | Male | 71 | 3 | 2 | Human Resources | 3 | Married | 6077 | 14814 | 3 | Y | No | 11 | 3 | 3 | 80 | 0 | 10 | 2 | 3 | 6 | 3 | 1 | 2 |
| 35 | No | Travel_Rarely | 992 | Research & Development | 1 | 3 | Medical | 1 | 1564 | 4 | Male | 68 | 2 | 1 | Laboratory Technician | 1 | Single | 2450 | 21731 | 1 | Y | No | 19 | 3 | 2 | 80 | 0 | 3 | 3 | 3 | 3 | 0 | 1 | 2 |
| 30 | No | Travel_Rarely | 1288 | Sales | 29 | 4 | Technical Degree | 1 | 1568 | 3 | Male | 33 | 3 | 3 | Sales Executive | 2 | Married | 9250 | 17799 | 3 | Y | No | 12 | 3 | 2 | 80 | 1 | 9 | 3 | 3 | 4 | 2 | 1 | 3 |
| 35 | Yes | Travel_Rarely | 104 | Research & Development | 2 | 3 | Life Sciences | 1 | 1569 | 1 | Female | 69 | 3 | 1 | Laboratory Technician | 1 | Divorced | 2074 | 26619 | 1 | Y | Yes | 12 | 3 | 4 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 53 | NA | Travel_Rarely | 607 | Research & Development | 2 | 5 | Technical Degree | 1 | 1572 | 3 | Female | 78 | 2 | 3 | Manufacturing Director | 4 | Married | 10169 | 14618 | 0 | Y | No | 16 | 3 | 2 | 80 | 1 | 34 | 4 | 3 | 33 | 7 | 1 | 9 |
| 38 | Yes | Travel_Rarely | 903 | Research & Development | 2 | 3 | Medical | 1 | 1573 | 3 | Male | 81 | 3 | 2 | Manufacturing Director | 2 | Married | 4855 | 7653 | 4 | Y | No | 11 | 3 | 1 | 80 | 2 | 7 | 2 | 3 | 5 | 2 | 1 | 4 |
| NA | No | Non-Travel | 1200 | Research & Development | 1 | 4 | Technical Degree | 1 | 1574 | 4 | Male | 62 | 3 | 2 | Research Scientist | 1 | Married | 4087 | 25174 | 4 | Y | No | 14 | 3 | 2 | 80 | 1 | 9 | 3 | 2 | 6 | 5 | 1 | 2 |
| 48 | No | Travel_Rarely | 1108 | Research & Development | 15 | 4 | Other | 1 | 1576 | 3 | Female | 65 | 3 | 1 | Research Scientist | 1 | Married | 2367 | 16530 | 8 | Y | No | 12 | 3 | 4 | 80 | 1 | 10 | 3 | 2 | 8 | 2 | 7 | 6 |
| 34 | No | Travel_Rarely | 479 | Research & Development | 7 | 4 | Medical | 1 | 1577 | 1 | Male | 35 | 3 | 1 | Research Scientist | 4 | Single | 2972 | 22061 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 1 | 4 | 1 | 1 | 0 | 0 | 0 |
| 55 | No | Travel_Rarely | 685 | Sales | 26 | 5 | Marketing | 1 | 1578 | 3 | Male | 60 | 2 | 5 | Manager | 4 | Married | 19586 | 23037 | 1 | Y | No | 21 | 4 | 3 | 80 | 1 | 36 | 3 | 3 | 36 | 6 | 2 | 13 |
| 34 | No | Travel_Rarely | 1351 | Research & Development | 1 | 4 | Life Sciences | 1 | 1580 | 2 | Male | 45 | 3 | 2 | Research Scientist | 4 | Married | 5484 | 13008 | 9 | Y | No | 17 | 3 | 2 | 80 | 1 | 9 | 3 | 2 | 2 | 2 | 2 | 1 |
| 26 | No | Travel_Rarely | 474 | Research & Development | 3 | 3 | Life Sciences | 1 | 1581 | 1 | Female | 89 | 3 | 1 | Research Scientist | 4 | Married | 2061 | 11133 | 1 | Y | No | 21 | 4 | 1 | 80 | 0 | 1 | 5 | 3 | 1 | 0 | 0 | 0 |
| 38 | No | Travel_Rarely | 1245 | Sales | 14 | 3 | Life Sciences | 1 | 1582 | 3 | Male | 80 | 3 | 2 | Sales Executive | 2 | Married | 9924 | 12355 | 0 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 3 | 3 | 9 | 8 | 7 | 7 |
| 38 | No | Travel_Rarely | 437 | Sales | 16 | 3 | Life Sciences | 1 | 1583 | 2 | Female | 90 | 3 | 2 | Sales Executive | 2 | Single | 4198 | 16379 | 2 | Y | No | 12 | 3 | 2 | 80 | 0 | 8 | 5 | 4 | 3 | 2 | 1 | 2 |
| 36 | No | Travel_Rarely | 884 | Sales | 1 | 4 | Life Sciences | 1 | 1585 | 2 | Female | 73 | 3 | 2 | Sales Executive | 3 | Single | 6815 | 21447 | 6 | Y | No | 13 | 3 | 1 | 80 | 0 | 15 | 5 | 3 | 1 | 0 | 0 | 0 |
| 29 | No | Travel_Rarely | 1370 | Research & Development | 3 | 1 | Medical | 1 | 1586 | 2 | Male | 87 | 3 | 1 | Laboratory Technician | 1 | Single | 4723 | 16213 | 1 | Y | Yes | 18 | 3 | 4 | 80 | 0 | 10 | 3 | 3 | 10 | 9 | 1 | 5 |
| 35 | No | Travel_Rarely | 670 | Research & Development | 10 | 4 | Medical | 1 | 1587 | 1 | Female | 51 | 3 | 2 | Healthcare Representative | 3 | Single | 6142 | 4223 | 3 | Y | Yes | 16 | 3 | 3 | 80 | 0 | 10 | 4 | 3 | 5 | 2 | 0 | 4 |
| 39 | No | Travel_Rarely | 1462 | Sales | 6 | 3 | Medical | 1 | 1588 | 4 | Male | 38 | 4 | 3 | Sales Executive | 3 | Married | 8237 | 4658 | 2 | Y | No | 11 | 3 | 1 | 80 | 1 | 11 | 3 | 3 | 7 | 6 | 7 | 6 |
| 29 | No | Travel_Frequently | 995 | Research & Development | 2 | 1 | Life Sciences | 1 | 1590 | 1 | Male | 87 | 3 | 2 | Healthcare Representative | 4 | Divorced | 8853 | 24483 | 1 | Y | No | 19 | 3 | 4 | 80 | 1 | 6 | 0 | 4 | 6 | 4 | 1 | 3 |
| 50 | No | Travel_Rarely | 264 | Sales | 9 | 3 | Marketing | 1 | 1591 | 3 | Male | 59 | 3 | 5 | Manager | 3 | Married | 19331 | 19519 | 4 | Y | Yes | 16 | 3 | 3 | 80 | 1 | 27 | 2 | 3 | 1 | 0 | 0 | 0 |
| 23 | No | Travel_Rarely | 977 | Research & Development | 10 | 3 | Technical Degree | 1 | 1592 | 4 | Male | 45 | 4 | 1 | Research Scientist | 3 | Married | 2073 | 12826 | 2 | Y | No | 16 | 3 | 4 | 80 | 1 | 4 | 2 | 3 | 2 | 2 | 2 | 2 |
| 36 | No | Travel_Frequently | 1302 | Research & Development | 6 | 4 | Life Sciences | 1 | 1594 | 1 | Male | 80 | 4 | 2 | Laboratory Technician | 1 | Married | 5562 | 19711 | 3 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 9 | 3 | 3 | 3 | 2 | 0 | 2 |
| 42 | No | Travel_Rarely | 1059 | Research & Development | 9 | 2 | Other | 1 | 1595 | 4 | Male | 93 | 2 | 5 | Manager | 4 | Single | 19613 | 26362 | 8 | Y | No | 22 | 4 | 4 | 80 | 0 | 24 | 2 | 3 | 1 | 0 | 0 | 1 |
| 35 | No | Travel_Rarely | 750 | Research & Development | 28 | 3 | Life Sciences | 1 | 1596 | 2 | Male | 46 | 4 | 2 | Laboratory Technician | 3 | Married | NA | 25348 | 1 | Y | No | 17 | 3 | 4 | 80 | 2 | 10 | 3 | 2 | 10 | 9 | 6 | 8 |
| 34 | No | Travel_Frequently | 653 | Research & Development | 10 | 4 | Technical Degree | 1 | 1597 | 4 | Male | 92 | 2 | 2 | Healthcare Representative | 3 | Married | 5063 | 15332 | 1 | Y | No | 14 | 3 | 2 | 80 | 1 | 8 | 3 | 2 | 8 | 2 | 7 | 7 |
| NA | No | Travel_Rarely | 118 | Sales | 14 | 2 | Life Sciences | 1 | 1598 | 4 | Female | 84 | 3 | 2 | Sales Executive | 1 | Married | 4639 | 11262 | 1 | Y | No | 15 | 3 | 3 | 80 | 1 | 5 | 2 | 3 | 5 | 4 | 1 | 2 |
| 43 | NA | Travel_Rarely | 990 | Research & Development | 27 | 3 | Technical Degree | 1 | 1599 | 4 | Male | 87 | 4 | 1 | Laboratory Technician | 2 | Divorced | 4876 | 5855 | 5 | Y | No | 12 | 3 | 3 | 80 | 1 | 8 | 0 | 3 | 6 | 4 | 0 | 2 |
| 35 | No | Travel_Rarely | 1349 | Research & Development | 7 | 2 | Life Sciences | 1 | 1601 | 3 | Male | 63 | 2 | 1 | Laboratory Technician | 4 | Married | NA | 7713 | 1 | Y | No | 18 | 3 | 4 | 80 | 1 | 1 | 5 | 2 | 1 | 0 | 0 | 1 |
| 46 | No | Travel_Rarely | 563 | Sales | 1 | 4 | Life Sciences | 1 | 1602 | 4 | Male | 56 | 4 | 4 | Manager | 1 | Single | 17567 | 3156 | 1 | Y | No | 15 | 3 | 2 | 80 | 0 | 27 | 5 | 1 | 26 | 0 | 0 | 12 |
| 28 | Yes | Travel_Rarely | 329 | Research & Development | 24 | 3 | Medical | 1 | 1604 | 3 | Male | 51 | 3 | 1 | Laboratory Technician | 2 | Married | 2408 | 7324 | 1 | Y | Yes | 17 | 3 | 3 | 80 | 3 | 1 | 3 | 3 | 1 | 1 | 0 | 0 |
| NA | NA | Non-Travel | 457 | Research & Development | 26 | 2 | Other | 1 | 1605 | 2 | Female | 85 | 2 | 1 | Research Scientist | 3 | Married | 2814 | 10293 | 1 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 4 | 2 | 2 | 4 | 2 | 1 | 3 |
| 50 | No | Travel_Frequently | 1234 | Research & Development | 20 | 5 | Medical | 1 | 1606 | 2 | Male | 41 | 3 | 4 | Healthcare Representative | 3 | Married | 11245 | 20689 | 2 | Y | Yes | 15 | 3 | 3 | 80 | 1 | 32 | 3 | 3 | 30 | 8 | 12 | 13 |
| NA | No | Travel_Rarely | 634 | Research & Development | 5 | 4 | Other | 1 | 1607 | 2 | Female | 35 | 4 | 1 | Research Scientist | 4 | Married | 3312 | 18783 | 3 | Y | No | 17 | 3 | 4 | 80 | 2 | 6 | 3 | 3 | 3 | 2 | 0 | 2 |
| 44 | No | Travel_Rarely | 1313 | Research & Development | 7 | 3 | Medical | 1 | 1608 | 2 | Female | 31 | 3 | 5 | Research Director | 4 | Divorced | 19049 | 3549 | 0 | Y | Yes | 14 | 3 | 4 | 80 | 1 | 23 | 4 | 2 | 22 | 7 | 1 | 10 |
| 30 | No | Travel_Rarely | 241 | Research & Development | 7 | 3 | Medical | 1 | 1609 | 2 | Male | 48 | 2 | 1 | Research Scientist | 2 | Married | 2141 | 5348 | 1 | Y | No | 12 | 3 | 2 | 80 | 1 | 6 | 3 | 2 | 6 | 4 | 1 | 1 |
| 45 | No | Travel_Rarely | 1015 | Research & Development | 5 | 5 | Medical | 1 | 1611 | 3 | Female | 50 | 1 | 2 | Laboratory Technician | 1 | Single | 5769 | 23447 | 1 | Y | Yes | 14 | 3 | 1 | 80 | 0 | 10 | 3 | 3 | 10 | 7 | 1 | 4 |
| NA | No | Non-Travel | 336 | Sales | 26 | 3 | Marketing | 1 | 1612 | 1 | Male | 52 | 2 | 2 | Sales Executive | 1 | Married | 4385 | 24162 | 1 | Y | No | 15 | 3 | 1 | 80 | 1 | 10 | 2 | 3 | 10 | 7 | 4 | 5 |
| 31 | No | Travel_Frequently | 715 | Sales | 2 | 4 | Other | 1 | 1613 | 4 | Male | 54 | 3 | 2 | Sales Executive | 1 | Single | 5332 | 21602 | 7 | Y | No | 13 | 3 | 4 | 80 | 0 | 10 | 3 | 3 | 5 | 2 | 0 | 3 |
| NA | No | Travel_Rarely | 559 | Research & Development | 12 | 4 | Life Sciences | 1 | 1614 | 3 | Female | 76 | 3 | 2 | Manufacturing Director | 3 | Married | NA | 12421 | 9 | Y | Yes | 12 | 3 | 2 | 80 | 2 | 7 | 2 | 3 | 3 | 2 | 1 | 1 |
| 34 | No | Travel_Frequently | 426 | Research & Development | 10 | 4 | Life Sciences | 1 | 1615 | 3 | Male | 42 | 4 | 2 | Manufacturing Director | 4 | Divorced | 4724 | 17000 | 1 | Y | No | 13 | 3 | 1 | 80 | 1 | 9 | 3 | 3 | 9 | 7 | 7 | 2 |
| 49 | No | Travel_Rarely | 722 | Research & Development | 25 | 4 | Life Sciences | 1 | 1617 | 3 | Female | 84 | 3 | 1 | Laboratory Technician | 1 | Married | NA | 22102 | 1 | Y | No | 14 | 3 | 4 | 80 | 1 | 10 | 3 | 2 | 9 | 6 | 1 | 4 |
| 39 | No | Travel_Rarely | 1387 | Research & Development | 10 | 5 | Medical | 1 | 1618 | 2 | Male | 76 | 3 | 2 | Manufacturing Director | 1 | Married | NA | 3835 | 2 | Y | No | 13 | 3 | 4 | 80 | 3 | 10 | 3 | 3 | 7 | 7 | 7 | 7 |
| 27 | No | Travel_Rarely | 1302 | Research & Development | 19 | 3 | Other | 1 | 1619 | 4 | Male | 67 | 2 | 1 | Laboratory Technician | 1 | Divorced | 4066 | 16290 | 1 | Y | No | 11 | 3 | 1 | 80 | 2 | 7 | 3 | 3 | 7 | 7 | 0 | 7 |
| 35 | No | Travel_Rarely | 819 | Research & Development | 18 | 5 | Life Sciences | 1 | 1621 | 2 | Male | 48 | 4 | 2 | Research Scientist | 1 | Married | 5208 | 26312 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 16 | 2 | 3 | 16 | 15 | 1 | 10 |
| 28 | No | Travel_Rarely | 580 | Research & Development | 27 | 3 | Medical | 1 | 1622 | 2 | Female | 39 | 1 | 2 | Manufacturing Director | 1 | Divorced | NA | 20460 | 0 | Y | No | 21 | 4 | 2 | 80 | 1 | 6 | 5 | 2 | 5 | 3 | 0 | 0 |
| 21 | No | Travel_Rarely | 546 | Research & Development | 5 | 1 | Medical | 1 | 1623 | 3 | Male | 97 | 3 | 1 | Research Scientist | 4 | Single | 3117 | 26009 | 1 | Y | No | 18 | 3 | 3 | 80 | 0 | 3 | 2 | 3 | 2 | 2 | 2 | 2 |
| 18 | Yes | Travel_Frequently | 544 | Sales | 3 | 2 | Medical | 1 | 1624 | 2 | Female | 70 | 3 | 1 | Sales Representative | 4 | Single | NA | 18420 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 0 | 2 | 4 | 0 | 0 | 0 | 0 |
| 47 | NA | Travel_Rarely | 1176 | Human Resources | 26 | 4 | Life Sciences | 1 | 1625 | 4 | Female | 98 | 3 | 5 | Manager | 3 | Married | 19658 | 5220 | 3 | Y | No | 11 | 3 | 3 | 80 | 1 | 27 | 2 | 3 | 5 | 2 | 1 | 0 |
| 39 | NA | Travel_Rarely | 170 | Research & Development | 3 | 2 | Medical | 1 | 1627 | 3 | Male | 76 | 2 | 2 | Laboratory Technician | 3 | Divorced | 3069 | 10302 | 0 | Y | No | 15 | 3 | 4 | 80 | 1 | 11 | 3 | 3 | 10 | 8 | 0 | 7 |
| 40 | No | Travel_Rarely | 884 | Research & Development | 15 | 3 | Life Sciences | 1 | 1628 | 1 | Female | 80 | 2 | 3 | Manufacturing Director | 3 | Married | NA | 25800 | 1 | Y | No | 13 | 3 | 4 | 80 | 2 | 18 | 2 | 3 | 18 | 15 | 14 | 12 |
| 35 | No | Non-Travel | 208 | Research & Development | 8 | 4 | Life Sciences | 1 | 1630 | 3 | Female | 52 | 3 | 2 | Healthcare Representative | 3 | Married | 4148 | 12250 | 1 | Y | No | 12 | 3 | 4 | 80 | 1 | 15 | 5 | 3 | 14 | 11 | 2 | 9 |
| NA | No | Travel_Rarely | 671 | Research & Development | 19 | 3 | Life Sciences | 1 | 1631 | 3 | Male | 85 | 3 | 2 | Manufacturing Director | 3 | Married | 5768 | 26493 | 3 | Y | No | 17 | 3 | 1 | 80 | 3 | 9 | 2 | 2 | 4 | 3 | 0 | 2 |
| 39 | No | Travel_Frequently | 711 | Research & Development | 4 | 3 | Medical | 1 | 1633 | 1 | Female | 81 | 3 | 2 | Manufacturing Director | 3 | Single | 5042 | 3140 | 0 | Y | No | 13 | 3 | 4 | 80 | 0 | 10 | 2 | 1 | 9 | 2 | 3 | 8 |
| 45 | No | Travel_Rarely | 1329 | Research & Development | 2 | 2 | Other | 1 | 1635 | 4 | Female | 59 | 2 | 2 | Manufacturing Director | 4 | Divorced | NA | 5388 | 1 | Y | No | 19 | 3 | 1 | 80 | 2 | 10 | 3 | 3 | 10 | 7 | 3 | 9 |
| 38 | No | Travel_Rarely | 397 | Research & Development | 2 | 2 | Medical | 1 | 1638 | 4 | Female | 54 | 2 | 3 | Manufacturing Director | 3 | Married | 7756 | 14199 | 3 | Y | Yes | 19 | 3 | 4 | 80 | 1 | 10 | 6 | 4 | 5 | 4 | 0 | 2 |
| 35 | Yes | Travel_Rarely | 737 | Sales | 10 | 3 | Medical | 1 | 1639 | 4 | Male | 55 | 2 | 3 | Sales Executive | 1 | Married | 10306 | 21530 | 9 | Y | No | 17 | 3 | 3 | 80 | 0 | 15 | 3 | 3 | 13 | 12 | 6 | 0 |
| 37 | No | Travel_Rarely | 1470 | Research & Development | 10 | 3 | Medical | 1 | 1640 | 2 | Female | 71 | 3 | 1 | Research Scientist | 2 | Married | 3936 | 9953 | 1 | Y | No | 11 | 3 | 1 | 80 | 1 | 8 | 2 | 1 | 8 | 4 | 7 | 7 |
| 40 | No | Travel_Rarely | 448 | Research & Development | 16 | 3 | Life Sciences | 1 | 1641 | 3 | Female | 84 | 3 | 3 | Manufacturing Director | 4 | Single | 7945 | 19948 | 6 | Y | Yes | 15 | 3 | 4 | 80 | 0 | 18 | 2 | 2 | 4 | 2 | 3 | 3 |
| 44 | No | Travel_Frequently | 602 | Human Resources | 1 | 5 | Human Resources | 1 | 1642 | 1 | Male | 37 | 3 | 2 | Human Resources | 4 | Married | 5743 | 10503 | 4 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 14 | 3 | 3 | 10 | 7 | 0 | 2 |
| 48 | No | Travel_Frequently | 365 | Research & Development | 4 | 5 | Medical | 1 | 1644 | 3 | Male | 89 | 2 | 4 | Manager | 4 | Married | 15202 | 5602 | 2 | Y | No | 25 | 4 | 2 | 80 | 1 | 23 | 3 | 3 | 2 | 2 | 2 | 2 |
| 35 | Yes | Travel_Rarely | 763 | Sales | 15 | 2 | Medical | 1 | 1645 | 1 | Male | 59 | 1 | 2 | Sales Executive | 4 | Divorced | 5440 | 22098 | 6 | Y | Yes | 14 | 3 | 4 | 80 | 2 | 7 | 2 | 2 | 2 | 2 | 2 | 2 |
| 24 | No | Travel_Frequently | 567 | Research & Development | 2 | 1 | Technical Degree | 1 | 1646 | 1 | Female | 32 | 3 | 1 | Research Scientist | 4 | Single | 3760 | 17218 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 0 | 6 | 2 | 3 | 6 | 3 | 1 | 3 |
| 27 | NA | Travel_Rarely | 486 | Research & Development | 8 | 3 | Medical | 1 | 1647 | 2 | Female | 86 | 4 | 1 | Research Scientist | 3 | Married | 3517 | 22490 | 7 | Y | No | 17 | 3 | 1 | 80 | 0 | 5 | 0 | 3 | 3 | 2 | 0 | 2 |
| 27 | No | Travel_Frequently | 591 | Research & Development | 2 | 3 | Medical | 1 | 1648 | 4 | Male | 87 | 3 | 1 | Research Scientist | 4 | Single | 2580 | 6297 | 2 | Y | No | 13 | 3 | 3 | 80 | 0 | 6 | 0 | 2 | 4 | 2 | 1 | 2 |
| 40 | Yes | Travel_Rarely | 1329 | Research & Development | 7 | 3 | Life Sciences | 1 | 1649 | 1 | Male | 73 | 3 | 1 | Laboratory Technician | 1 | Single | 2166 | 3339 | 3 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 10 | 3 | 1 | 4 | 2 | 0 | 3 |
| 29 | No | Travel_Rarely | 469 | Sales | 10 | 3 | Medical | 1 | 1650 | 3 | Male | 42 | 2 | 2 | Sales Executive | 3 | Single | NA | 23413 | 9 | Y | No | 11 | 3 | 3 | 80 | 0 | 8 | 2 | 3 | 5 | 2 | 1 | 4 |
| 36 | No | Travel_Rarely | 711 | Research & Development | 5 | 4 | Life Sciences | 1 | 1651 | 2 | Female | 42 | 3 | 3 | Healthcare Representative | 1 | Married | 8008 | 22792 | 4 | Y | No | 12 | 3 | 3 | 80 | 2 | 9 | 6 | 3 | 3 | 2 | 0 | 2 |
| 25 | No | Travel_Frequently | 772 | Research & Development | 2 | 1 | Life Sciences | 1 | 1653 | 4 | Male | 77 | 4 | 2 | Manufacturing Director | 3 | Divorced | 5206 | 4973 | 1 | Y | No | 17 | 3 | 3 | 80 | 2 | 7 | 6 | 3 | 7 | 7 | 0 | 7 |
| 39 | No | Travel_Rarely | 492 | Research & Development | 12 | 3 | Medical | 1 | 1654 | 4 | Male | 66 | 3 | 2 | Manufacturing Director | 2 | Married | 5295 | 7693 | 4 | Y | No | 21 | 4 | 3 | 80 | 0 | 7 | 3 | 3 | 5 | 4 | 1 | 0 |
| 49 | No | Travel_Rarely | 301 | Research & Development | 22 | 4 | Other | 1 | 1655 | 1 | Female | 72 | 3 | 4 | Research Director | 2 | Married | NA | 3498 | 3 | Y | No | 16 | 3 | 2 | 80 | 2 | 27 | 2 | 3 | 4 | 2 | 1 | 2 |
| 50 | No | Travel_Rarely | 813 | Research & Development | 17 | 5 | Life Sciences | 1 | 1656 | 4 | Female | 50 | 2 | 3 | Research Director | 1 | Divorced | 13269 | 21981 | 5 | Y | No | 15 | 3 | 3 | 80 | 3 | 19 | 3 | 3 | 14 | 11 | 1 | 11 |
| 20 | No | Travel_Rarely | 1141 | Sales | 2 | 3 | Medical | 1 | 1657 | 3 | Female | 31 | 3 | 1 | Sales Representative | 3 | Single | NA | 13251 | 1 | Y | No | 19 | 3 | 1 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 2 | 2 |
| 34 | No | Travel_Rarely | 1130 | Research & Development | 3 | 3 | Life Sciences | 1 | 1658 | 4 | Female | 66 | 3 | 2 | Research Scientist | 2 | Divorced | 5433 | 19332 | 1 | Y | No | 12 | 3 | 3 | 80 | 1 | 11 | 2 | 3 | 11 | 8 | 7 | 9 |
| 36 | No | Travel_Rarely | 311 | Research & Development | 7 | 3 | Life Sciences | 1 | 1659 | 1 | Male | 77 | 3 | 1 | Laboratory Technician | 2 | Single | NA | 10950 | 2 | Y | No | 11 | 3 | 3 | 80 | 0 | 15 | 4 | 3 | 4 | 3 | 1 | 3 |
| 49 | No | Travel_Rarely | 465 | Research & Development | 6 | 1 | Life Sciences | 1 | 1661 | 3 | Female | 41 | 2 | 4 | Healthcare Representative | 3 | Married | 13966 | 11652 | 2 | Y | Yes | 19 | 3 | 2 | 80 | 1 | 30 | 3 | 3 | 15 | 11 | 2 | 12 |
| 36 | No | Non-Travel | 894 | Research & Development | 1 | 4 | Medical | 1 | 1662 | 4 | Female | 33 | 2 | 2 | Manufacturing Director | 3 | Married | 4374 | 15411 | 0 | Y | No | 15 | 3 | 3 | 80 | 0 | 4 | 6 | 3 | 3 | 2 | 1 | 2 |
| 36 | No | Travel_Rarely | 1040 | Research & Development | 3 | 2 | Life Sciences | 1 | 1664 | 4 | Male | 79 | 4 | 2 | Healthcare Representative | 1 | Divorced | 6842 | 26308 | 6 | Y | No | 20 | 4 | 1 | 80 | 1 | 13 | 3 | 3 | 5 | 4 | 0 | 4 |
| 54 | No | Travel_Rarely | 584 | Research & Development | 22 | 5 | Medical | 1 | 1665 | 2 | Female | 91 | 3 | 4 | Manager | 3 | Married | 17426 | 18685 | 3 | Y | No | 25 | 4 | 3 | 80 | 1 | 36 | 6 | 3 | 10 | 8 | 4 | 7 |
| 43 | NA | Travel_Rarely | 1291 | Research & Development | 15 | 2 | Life Sciences | 1 | 1666 | 3 | Male | 65 | 2 | 4 | Research Director | 3 | Married | 17603 | 3525 | 1 | Y | No | 24 | 4 | 1 | 80 | 1 | 14 | 3 | 3 | 14 | 10 | 6 | 11 |
| 35 | Yes | Travel_Frequently | 880 | Sales | 12 | 4 | Other | 1 | 1667 | 4 | Male | 36 | 3 | 2 | Sales Executive | 4 | Single | 4581 | 10414 | 3 | Y | Yes | 24 | 4 | 1 | 80 | 0 | 13 | 2 | 4 | 11 | 9 | 6 | 7 |
| 38 | No | Travel_Frequently | 1189 | Research & Development | 1 | 3 | Life Sciences | 1 | 1668 | 4 | Male | 90 | 3 | 2 | Research Scientist | 4 | Married | 4735 | 9867 | 7 | Y | No | 15 | 3 | 4 | 80 | 2 | 19 | 4 | 4 | 13 | 11 | 2 | 9 |
| 29 | No | Travel_Rarely | 991 | Sales | 5 | 3 | Medical | 1 | 1669 | 1 | Male | 43 | 2 | 2 | Sales Executive | 2 | Divorced | NA | 3356 | 1 | Y | Yes | 13 | 3 | 2 | 80 | 1 | 10 | 3 | 2 | 10 | 0 | 0 | 9 |
| 33 | No | Travel_Rarely | 392 | Sales | 2 | 4 | Medical | 1 | 1670 | 4 | Male | 93 | 3 | 2 | Sales Executive | 4 | Divorced | 5505 | 3921 | 1 | Y | No | 14 | 3 | 3 | 80 | 2 | 6 | 5 | 3 | 6 | 2 | 0 | 4 |
| 32 | No | Travel_Rarely | 977 | Research & Development | 2 | 3 | Medical | 1 | 1671 | 4 | Male | 45 | 3 | 2 | Research Scientist | 2 | Divorced | 5470 | 25518 | 0 | Y | No | 13 | 3 | 3 | 80 | 2 | 10 | 4 | 2 | 9 | 5 | 1 | 6 |
| 31 | No | Travel_Rarely | 1112 | Sales | 5 | 4 | Life Sciences | 1 | 1673 | 1 | Female | 67 | 3 | 2 | Sales Executive | 4 | Married | 5476 | 22589 | 1 | Y | No | 11 | 3 | 1 | 80 | 2 | 10 | 2 | 3 | 10 | 0 | 0 | 2 |
| 49 | No | Travel_Rarely | 464 | Research & Development | 16 | 3 | Medical | 1 | 1674 | 4 | Female | 74 | 3 | 1 | Laboratory Technician | 1 | Divorced | 2587 | 24941 | 4 | Y | Yes | 16 | 3 | 2 | 80 | 1 | 17 | 2 | 2 | 2 | 2 | 2 | 2 |
| 38 | No | Travel_Frequently | 148 | Research & Development | 2 | 3 | Medical | 1 | 1675 | 4 | Female | 42 | 2 | 1 | Laboratory Technician | 2 | Single | 2440 | 23826 | 1 | Y | No | 22 | 4 | 2 | 80 | 0 | 4 | 3 | 3 | 4 | 3 | 3 | 3 |
| 47 | No | Travel_Rarely | 1225 | Sales | 2 | 4 | Life Sciences | 1 | 1676 | 2 | Female | 47 | 4 | 4 | Manager | 2 | Divorced | 15972 | 21086 | 6 | Y | No | 14 | 3 | 3 | 80 | 3 | 29 | 2 | 3 | 3 | 2 | 1 | 2 |
| 49 | No | Travel_Rarely | 809 | Research & Development | 1 | 3 | Life Sciences | 1 | 1677 | 3 | Male | 36 | 3 | 4 | Manager | 3 | Single | 15379 | 22384 | 4 | Y | No | 14 | 3 | 1 | 80 | 0 | 23 | 2 | 3 | 8 | 7 | 0 | 0 |
| 41 | No | Travel_Rarely | 1206 | Sales | 23 | 2 | Life Sciences | 1 | 1678 | 4 | Male | 80 | 3 | 3 | Sales Executive | 3 | Single | 7082 | 11591 | 3 | Y | Yes | 16 | 3 | 4 | 80 | 0 | 21 | 2 | 3 | 2 | 0 | 0 | 2 |
| 20 | NA | Travel_Rarely | 727 | Sales | 9 | 1 | Life Sciences | 1 | 1680 | 4 | Male | 54 | 3 | 1 | Sales Representative | 1 | Single | 2728 | 21082 | 1 | Y | No | 11 | 3 | 1 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 0 | 2 |
| 33 | No | Non-Travel | 530 | Sales | 16 | 3 | Life Sciences | 1 | 1681 | 3 | Female | 36 | 3 | 2 | Sales Executive | 4 | Divorced | 5368 | 16130 | 1 | Y | Yes | 25 | 4 | 3 | 80 | 1 | 7 | 2 | 3 | 6 | 5 | 1 | 2 |
| 36 | No | Travel_Rarely | 1351 | Research & Development | 26 | 4 | Life Sciences | 1 | 1682 | 1 | Male | 80 | 3 | 2 | Healthcare Representative | 3 | Married | 5347 | 7419 | 6 | Y | No | 14 | 3 | 2 | 80 | 2 | 10 | 2 | 2 | 3 | 2 | 0 | 2 |
| 44 | No | Travel_Rarely | 528 | Human Resources | 1 | 3 | Life Sciences | 1 | 1683 | 3 | Female | 44 | 3 | 1 | Human Resources | 4 | Divorced | 3195 | 4167 | 4 | Y | Yes | 18 | 3 | 1 | 80 | 3 | 8 | 2 | 3 | 2 | 2 | 2 | 2 |
| 23 | Yes | Travel_Rarely | 1320 | Research & Development | 8 | 1 | Medical | 1 | 1684 | 4 | Male | 93 | 2 | 1 | Laboratory Technician | 3 | Single | 3989 | 20586 | 1 | Y | Yes | 11 | 3 | 1 | 80 | 0 | 5 | 2 | 3 | 5 | 4 | 1 | 2 |
| 38 | No | Travel_Rarely | 1495 | Research & Development | 4 | 2 | Medical | 1 | 1687 | 4 | Female | 87 | 3 | 1 | Laboratory Technician | 3 | Married | 3306 | 26176 | 7 | Y | No | 19 | 3 | 4 | 80 | 1 | 7 | 5 | 2 | 0 | 0 | 0 | 0 |
| 53 | No | Travel_Rarely | 1395 | Research & Development | 24 | 4 | Medical | 1 | 1689 | 2 | Male | 48 | 4 | 3 | Healthcare Representative | 4 | Married | 7005 | 3458 | 3 | Y | No | 15 | 3 | 3 | 80 | 0 | 11 | 2 | 3 | 4 | 3 | 1 | 2 |
| 48 | Yes | Travel_Frequently | 708 | Sales | 7 | 2 | Medical | 1 | 1691 | 4 | Female | 95 | 3 | 1 | Sales Representative | 3 | Married | 2655 | 11740 | 2 | Y | Yes | 11 | 3 | 3 | 80 | 2 | 19 | 3 | 3 | 9 | 7 | 7 | 7 |
| 32 | Yes | Travel_Rarely | 1259 | Research & Development | 2 | 4 | Life Sciences | 1 | 1692 | 4 | Male | 95 | 3 | 1 | Laboratory Technician | 2 | Single | 1393 | 24852 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 26 | No | Non-Travel | 786 | Research & Development | 7 | 3 | Medical | 1 | 1693 | 4 | Male | 76 | 3 | 1 | Laboratory Technician | 4 | Single | 2570 | 11925 | 1 | Y | No | 20 | 4 | 3 | 80 | 0 | 7 | 5 | 3 | 7 | 7 | 5 | 7 |
| 55 | No | Travel_Rarely | 1441 | Research & Development | 22 | 3 | Technical Degree | 1 | 1694 | 1 | Male | 94 | 2 | 1 | Research Scientist | 2 | Divorced | 3537 | 23737 | 5 | Y | No | 12 | 3 | 4 | 80 | 1 | 8 | 1 | 3 | 4 | 2 | 1 | 2 |
| 34 | No | Travel_Rarely | 1157 | Research & Development | 5 | 2 | Medical | 1 | 1696 | 2 | Male | 57 | 2 | 2 | Laboratory Technician | 4 | Married | 3986 | 11912 | 1 | Y | No | 14 | 3 | 3 | 80 | 1 | 15 | 3 | 4 | 15 | 10 | 4 | 13 |
| 60 | No | Travel_Rarely | 370 | Research & Development | 1 | 4 | Medical | 1 | 1697 | 3 | Male | 92 | 1 | 3 | Healthcare Representative | 4 | Divorced | NA | 20467 | 3 | Y | No | 20 | 4 | 3 | 80 | 1 | 19 | 2 | 4 | 1 | 0 | 0 | 0 |
| 33 | No | Travel_Rarely | 267 | Research & Development | 21 | 3 | Medical | 1 | 1698 | 2 | Male | 79 | 4 | 1 | Laboratory Technician | 2 | Married | 2028 | 13637 | 1 | Y | No | 18 | 3 | 4 | 80 | 3 | 14 | 6 | 3 | 14 | 11 | 2 | 13 |
| 37 | No | Travel_Frequently | 1278 | Sales | 1 | 4 | Medical | 1 | 1700 | 3 | Male | 31 | 1 | 2 | Sales Executive | 4 | Divorced | 9525 | 7677 | 1 | Y | No | 14 | 3 | 3 | 80 | 2 | 6 | 2 | 2 | 6 | 3 | 1 | 3 |
| 34 | NA | Travel_Rarely | 678 | Research & Development | 19 | 3 | Life Sciences | 1 | 1701 | 2 | Female | 35 | 2 | 1 | Research Scientist | 4 | Married | 2929 | 20338 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 10 | 3 | 3 | 10 | 9 | 8 | 7 |
| 23 | Yes | Travel_Rarely | 427 | Sales | 7 | 3 | Life Sciences | 1 | 1702 | 3 | Male | 99 | 3 | 1 | Sales Representative | 4 | Divorced | 2275 | 25103 | 1 | Y | Yes | 21 | 4 | 2 | 80 | 1 | 3 | 2 | 3 | 3 | 2 | 0 | 2 |
| 44 | No | Travel_Rarely | 921 | Research & Development | 2 | 3 | Life Sciences | 1 | 1703 | 3 | Female | 96 | 4 | 3 | Healthcare Representative | 4 | Married | NA | 14810 | 1 | Y | Yes | 19 | 3 | 2 | 80 | 1 | 9 | 2 | 3 | 8 | 7 | 6 | 7 |
| 35 | No | Travel_Frequently | 146 | Research & Development | 2 | 4 | Medical | 1 | 1704 | 1 | Male | 79 | 2 | 1 | Research Scientist | 4 | Single | 4930 | 13970 | 0 | Y | Yes | 14 | 3 | 3 | 80 | 0 | 6 | 2 | 4 | 5 | 4 | 1 | 4 |
| 43 | No | Travel_Rarely | 1179 | Sales | 2 | 3 | Medical | 1 | 1706 | 4 | Male | 73 | 3 | 2 | Sales Executive | 4 | Married | 7847 | 6069 | 1 | Y | Yes | 17 | 3 | 1 | 80 | 1 | 10 | 3 | 3 | 10 | 9 | 8 | 8 |
| 24 | No | Travel_Rarely | 581 | Research & Development | 9 | 3 | Medical | 1 | 1707 | 3 | Male | 62 | 4 | 1 | Research Scientist | 3 | Married | 4401 | 17616 | 1 | Y | No | 16 | 3 | 4 | 80 | 1 | 5 | 1 | 3 | 5 | 3 | 0 | 4 |
| NA | No | Travel_Rarely | 918 | Sales | 6 | 3 | Marketing | 1 | 1708 | 4 | Male | 35 | 3 | 3 | Sales Executive | 3 | Single | 9241 | 15869 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 10 | 3 | 3 | 10 | 8 | 8 | 7 |
| 29 | No | Travel_Rarely | 1082 | Research & Development | 9 | 4 | Medical | 1 | 1709 | 4 | Female | 43 | 3 | 1 | Laboratory Technician | 3 | Married | 2974 | 25412 | 9 | Y | No | 17 | 3 | 3 | 80 | 1 | 9 | 2 | 3 | 5 | 3 | 1 | 2 |
| 36 | No | Travel_Rarely | 530 | Sales | 2 | 4 | Life Sciences | 1 | 1710 | 3 | Female | 51 | 3 | 2 | Sales Representative | 4 | Single | NA | 7439 | 3 | Y | No | 15 | 3 | 3 | 80 | 0 | 17 | 2 | 2 | 13 | 7 | 6 | 7 |
| 45 | No | Non-Travel | 1238 | Research & Development | 1 | 1 | Life Sciences | 1 | 1712 | 3 | Male | 74 | 2 | 3 | Healthcare Representative | 3 | Married | 10748 | 3395 | 3 | Y | No | 23 | 4 | 4 | 80 | 1 | 25 | 3 | 2 | 23 | 15 | 14 | 4 |
| 24 | Yes | Travel_Rarely | 240 | Human Resources | 22 | 1 | Human Resources | 1 | 1714 | 4 | Male | 58 | 1 | 1 | Human Resources | 3 | Married | 1555 | 11585 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 47 | Yes | Travel_Frequently | 1093 | Sales | 9 | 3 | Life Sciences | 1 | 1716 | 3 | Male | 82 | 1 | 4 | Sales Executive | 3 | Married | 12936 | 24164 | 7 | Y | No | 11 | 3 | 3 | 80 | 0 | 25 | 3 | 1 | 23 | 5 | 14 | 10 |
| 26 | No | Travel_Rarely | 390 | Research & Development | 17 | 4 | Medical | 1 | 1718 | 4 | Male | 62 | 1 | 1 | Laboratory Technician | 3 | Married | 2305 | 6217 | 1 | Y | No | 15 | 3 | 3 | 80 | 3 | 3 | 3 | 4 | 3 | 2 | 0 | 2 |
| NA | NA | Travel_Rarely | 1005 | Research & Development | 28 | 2 | Technical Degree | 1 | 1719 | 4 | Female | 48 | 2 | 4 | Research Director | 2 | Single | 16704 | 17119 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 21 | 2 | 3 | 21 | 6 | 8 | 6 |
| 32 | No | Travel_Frequently | 585 | Research & Development | 10 | 3 | Life Sciences | 1 | 1720 | 1 | Male | 56 | 3 | 1 | Research Scientist | 3 | Married | 3433 | 17360 | 6 | Y | No | 13 | 3 | 1 | 80 | 1 | 10 | 3 | 2 | 5 | 2 | 1 | 3 |
| 31 | No | Travel_Rarely | 741 | Research & Development | 2 | 4 | Life Sciences | 1 | 1721 | 2 | Male | 69 | 3 | 1 | Laboratory Technician | 3 | Married | 3477 | 18103 | 1 | Y | No | 14 | 3 | 4 | 80 | 1 | 6 | 2 | 4 | 5 | 2 | 0 | 3 |
| 41 | No | Non-Travel | 552 | Human Resources | 4 | 3 | Human Resources | 1 | 1722 | 3 | Male | 60 | 1 | 2 | Human Resources | 2 | Married | 6430 | 20794 | 6 | Y | No | 19 | 3 | 2 | 80 | 1 | 10 | 4 | 3 | 3 | 2 | 1 | 2 |
| 40 | No | Travel_Rarely | 369 | Research & Development | 8 | 2 | Life Sciences | 1 | 1724 | 2 | Female | 92 | 3 | 2 | Manufacturing Director | 1 | Married | 6516 | 5041 | 2 | Y | Yes | 16 | 3 | 2 | 80 | 1 | 18 | 3 | 3 | 1 | 0 | 0 | 0 |
| 24 | No | Travel_Rarely | 506 | Research & Development | 29 | 1 | Medical | 1 | 1725 | 2 | Male | 91 | 3 | 1 | Laboratory Technician | 1 | Divorced | 3907 | 3622 | 1 | Y | No | 13 | 3 | 2 | 80 | 3 | 6 | 2 | 4 | 6 | 2 | 1 | 2 |
| 46 | No | Travel_Rarely | 717 | Research & Development | 13 | 4 | Life Sciences | 1 | 1727 | 3 | Male | 34 | 3 | 2 | Healthcare Representative | 2 | Single | 5562 | 9697 | 6 | Y | No | 14 | 3 | 4 | 80 | 0 | 19 | 3 | 3 | 10 | 7 | 0 | 9 |
| 35 | No | Travel_Rarely | 1370 | Research & Development | 27 | 4 | Life Sciences | 1 | 1728 | 4 | Male | 49 | 3 | 2 | Manufacturing Director | 3 | Married | 6883 | 5151 | 2 | Y | No | 16 | 3 | 2 | 80 | 1 | 17 | 3 | 3 | 7 | 7 | 0 | 7 |
| 30 | No | Travel_Rarely | 793 | Research & Development | 16 | 1 | Life Sciences | 1 | 1729 | 2 | Male | 33 | 3 | 1 | Research Scientist | 4 | Married | 2862 | 3811 | 1 | Y | No | 12 | 3 | 2 | 80 | 1 | 10 | 2 | 2 | 10 | 0 | 0 | 8 |
| 47 | No | Non-Travel | 543 | Sales | 2 | 4 | Marketing | 1 | 1731 | 3 | Male | 87 | 3 | 2 | Sales Executive | 2 | Married | 4978 | 3536 | 7 | Y | No | 11 | 3 | 4 | 80 | 1 | 4 | 3 | 1 | 1 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 1277 | Sales | 2 | 3 | Life Sciences | 1 | 1732 | 3 | Male | 74 | 3 | 3 | Sales Executive | 4 | Divorced | 10368 | 5596 | 4 | Y | Yes | 12 | 3 | 2 | 80 | 1 | 13 | 5 | 2 | 10 | 6 | 0 | 3 |
| 36 | Yes | Travel_Rarely | 1456 | Sales | 13 | 5 | Marketing | 1 | 1733 | 2 | Male | 96 | 2 | 2 | Sales Executive | 1 | Divorced | 6134 | 8658 | 5 | Y | Yes | 13 | 3 | 2 | 80 | 3 | 16 | 3 | 3 | 2 | 2 | 2 | 2 |
| 32 | NA | Travel_Rarely | 964 | Sales | 1 | 2 | Life Sciences | 1 | 1734 | 1 | Male | 34 | 1 | 2 | Sales Executive | 2 | Single | 6735 | 12147 | 6 | Y | No | 15 | 3 | 2 | 80 | 0 | 10 | 2 | 3 | 0 | 0 | 0 | 0 |
| 23 | No | Travel_Rarely | 160 | Research & Development | 4 | 1 | Medical | 1 | 1735 | 3 | Female | 51 | 3 | 1 | Laboratory Technician | 2 | Single | 3295 | 12862 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 3 | 3 | 1 | 3 | 2 | 1 | 2 |
| 31 | No | Travel_Frequently | 163 | Research & Development | 24 | 1 | Technical Degree | 1 | 1736 | 4 | Female | 30 | 3 | 2 | Manufacturing Director | 4 | Single | 5238 | 6670 | 2 | Y | No | 20 | 4 | 4 | 80 | 0 | 9 | 3 | 2 | 5 | 4 | 1 | 4 |
| 39 | No | Non-Travel | 792 | Research & Development | 1 | 3 | Life Sciences | 1 | 1737 | 4 | Male | 77 | 3 | 2 | Laboratory Technician | 4 | Married | 6472 | 8989 | 1 | Y | Yes | 15 | 3 | 4 | 80 | 1 | 9 | 2 | 3 | 9 | 8 | 5 | 8 |
| 32 | No | Travel_Rarely | 371 | Sales | 19 | 3 | Life Sciences | 1 | 1739 | 4 | Male | 80 | 1 | 3 | Sales Executive | 3 | Married | 9610 | 3840 | 3 | Y | No | 13 | 3 | 3 | 80 | 1 | 10 | 2 | 1 | 4 | 3 | 0 | 2 |
| 40 | No | Travel_Rarely | 611 | Sales | 7 | 4 | Medical | 1 | 1740 | 2 | Male | 88 | 3 | 5 | Manager | 2 | Single | 19833 | 4349 | 1 | Y | No | 14 | 3 | 2 | 80 | 0 | 21 | 3 | 2 | 21 | 8 | 12 | 8 |
| 45 | No | Travel_Rarely | 176 | Human Resources | 4 | 3 | Life Sciences | 1 | 1744 | 3 | Female | 56 | 1 | 3 | Human Resources | 3 | Married | 9756 | 6595 | 4 | Y | No | 21 | 4 | 3 | 80 | 2 | 9 | 2 | 4 | 5 | 0 | 0 | 3 |
| 30 | No | Travel_Frequently | 1312 | Research & Development | 2 | 4 | Technical Degree | 1 | 1745 | 4 | Female | 78 | 2 | 1 | Research Scientist | 1 | Single | 4968 | 26427 | 0 | Y | No | 16 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 9 | 7 | 0 | 7 |
| 24 | No | Travel_Frequently | 897 | Human Resources | 10 | 3 | Medical | 1 | 1746 | 1 | Male | 59 | 3 | 1 | Human Resources | 4 | Married | 2145 | 2097 | 0 | Y | No | 14 | 3 | 4 | 80 | 1 | 3 | 2 | 3 | 2 | 2 | 2 | 1 |
| 30 | Yes | Travel_Frequently | 600 | Human Resources | 8 | 3 | Human Resources | 1 | 1747 | 3 | Female | 66 | 2 | 1 | Human Resources | 4 | Divorced | NA | 9732 | 6 | Y | No | 11 | 3 | 3 | 80 | 1 | 6 | 0 | 2 | 4 | 2 | 1 | 2 |
| 31 | No | Travel_Rarely | 1003 | Sales | 5 | 3 | Technical Degree | 1 | 1749 | 1 | Male | 51 | 3 | 2 | Sales Executive | 3 | Married | 8346 | 20943 | 1 | Y | No | 19 | 3 | 3 | 80 | 1 | 6 | 3 | 3 | 5 | 2 | 0 | 2 |
| 27 | No | Travel_Rarely | 1054 | Research & Development | 8 | 3 | Medical | 1 | 1751 | 3 | Female | 67 | 3 | 1 | Research Scientist | 4 | Single | 3445 | 6152 | 1 | Y | No | 11 | 3 | 3 | 80 | 0 | 6 | 5 | 2 | 6 | 2 | 1 | 4 |
| 29 | Yes | Travel_Rarely | 428 | Sales | 9 | 3 | Marketing | 1 | 1752 | 2 | Female | 52 | 1 | 1 | Sales Representative | 2 | Single | 2760 | 14630 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 2 | 3 | 3 | 2 | 2 | 2 | 2 |
| 29 | NA | Travel_Frequently | 461 | Research & Development | 1 | 3 | Life Sciences | 1 | 1753 | 4 | Male | 70 | 4 | 2 | Healthcare Representative | 3 | Single | 6294 | 23060 | 8 | Y | Yes | 12 | 3 | 4 | 80 | 0 | 10 | 5 | 4 | 3 | 2 | 0 | 2 |
| 30 | No | Travel_Rarely | 979 | Sales | 15 | 2 | Marketing | 1 | 1754 | 3 | Male | 94 | 2 | 3 | Sales Executive | 1 | Divorced | 7140 | 3088 | 2 | Y | No | 11 | 3 | 1 | 80 | 1 | 12 | 2 | 3 | 7 | 7 | 1 | 7 |
| 34 | No | Travel_Rarely | 181 | Research & Development | 2 | 4 | Medical | 1 | 1755 | 4 | Male | 97 | 4 | 1 | Research Scientist | 4 | Married | 2932 | 5586 | 0 | Y | Yes | 14 | 3 | 1 | 80 | 3 | 6 | 3 | 3 | 5 | 0 | 1 | 2 |
| 33 | No | Non-Travel | 1283 | Sales | 2 | 3 | Marketing | 1 | 1756 | 4 | Female | 62 | 3 | 2 | Sales Executive | 2 | Single | 5147 | 10697 | 8 | Y | No | 15 | 3 | 4 | 80 | 0 | 13 | 2 | 2 | 11 | 7 | 1 | 7 |
| 49 | No | Travel_Rarely | 1313 | Sales | 11 | 4 | Marketing | 1 | 1757 | 4 | Female | 80 | 3 | 2 | Sales Executive | 4 | Single | 4507 | 8191 | 3 | Y | No | 12 | 3 | 3 | 80 | 0 | 8 | 1 | 4 | 5 | 1 | 0 | 4 |
| 33 | Yes | Travel_Rarely | 211 | Sales | 16 | 3 | Life Sciences | 1 | 1758 | 1 | Female | 74 | 3 | 3 | Sales Executive | 1 | Single | NA | 10092 | 2 | Y | Yes | 20 | 4 | 3 | 80 | 0 | 11 | 2 | 2 | 0 | 0 | 0 | 0 |
| 38 | No | Travel_Frequently | 594 | Research & Development | 2 | 2 | Medical | 1 | 1760 | 3 | Female | 75 | 2 | 1 | Laboratory Technician | 2 | Married | 2468 | 15963 | 4 | Y | No | 14 | 3 | 2 | 80 | 1 | 9 | 4 | 2 | 6 | 1 | 0 | 5 |
| 31 | Yes | Travel_Rarely | 1079 | Sales | 16 | 4 | Marketing | 1 | 1761 | 1 | Male | 70 | 3 | 3 | Sales Executive | 3 | Married | 8161 | 19002 | 2 | Y | No | 13 | 3 | 1 | 80 | 3 | 10 | 2 | 3 | 1 | 0 | 0 | 0 |
| 29 | No | Travel_Rarely | 590 | Research & Development | 4 | 3 | Technical Degree | 1 | 1762 | 4 | Female | 91 | 2 | 1 | Research Scientist | 1 | Divorced | 2109 | 10007 | 1 | Y | No | 13 | 3 | 3 | 80 | 1 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 30 | NA | Travel_Rarely | 305 | Research & Development | 16 | 3 | Life Sciences | 1 | 1763 | 3 | Male | 58 | 4 | 2 | Healthcare Representative | 3 | Married | NA | 9128 | 3 | Y | No | 16 | 3 | 3 | 80 | 1 | 10 | 3 | 3 | 7 | 0 | 1 | 7 |
| 32 | No | Non-Travel | 953 | Research & Development | 5 | 4 | Technical Degree | 1 | 1764 | 2 | Male | 65 | 3 | 1 | Research Scientist | 2 | Single | 2718 | 17674 | 2 | Y | No | 14 | 3 | 2 | 80 | 0 | 12 | 3 | 3 | 7 | 7 | 0 | 7 |
| 38 | No | Travel_Rarely | 833 | Research & Development | 18 | 3 | Medical | 1 | 1766 | 2 | Male | 60 | 1 | 2 | Healthcare Representative | 4 | Married | 5811 | 24539 | 3 | Y | Yes | 16 | 3 | 3 | 80 | 1 | 15 | 2 | 3 | 1 | 0 | 1 | 0 |
| 43 | Yes | Travel_Frequently | 807 | Research & Development | 17 | 3 | Technical Degree | 1 | 1767 | 3 | Male | 38 | 2 | 1 | Research Scientist | 3 | Married | 2437 | 15587 | 9 | Y | Yes | 16 | 3 | 4 | 80 | 1 | 6 | 4 | 3 | 1 | 0 | 0 | 0 |
| NA | No | Travel_Rarely | 855 | Research & Development | 12 | 3 | Medical | 1 | 1768 | 2 | Male | 57 | 3 | 1 | Laboratory Technician | 2 | Divorced | 2766 | 8952 | 8 | Y | No | 22 | 4 | 2 | 80 | 3 | 7 | 6 | 2 | 5 | 3 | 0 | 4 |
| 55 | NA | Travel_Rarely | 478 | Research & Development | 2 | 3 | Medical | 1 | 1770 | 3 | Male | 60 | 2 | 5 | Research Director | 1 | Married | 19038 | 19805 | 8 | Y | No | 12 | 3 | 2 | 80 | 3 | 34 | 2 | 3 | 1 | 0 | 0 | 0 |
| 33 | NA | Non-Travel | 775 | Research & Development | 4 | 3 | Technical Degree | 1 | 1771 | 4 | Male | 90 | 3 | 2 | Research Scientist | 2 | Divorced | 3055 | 6194 | 5 | Y | No | 15 | 3 | 4 | 80 | 2 | 11 | 2 | 2 | 9 | 8 | 1 | 7 |
| 41 | No | Travel_Rarely | 548 | Research & Development | 9 | 4 | Life Sciences | 1 | 1772 | 3 | Male | 94 | 3 | 1 | Laboratory Technician | 1 | Divorced | 2289 | 20520 | 1 | Y | No | 20 | 4 | 2 | 80 | 2 | 5 | 2 | 3 | 5 | 3 | 0 | 4 |
| 34 | No | Non-Travel | 1375 | Sales | 10 | 3 | Life Sciences | 1 | 1774 | 4 | Male | 87 | 3 | 2 | Sales Executive | 3 | Divorced | NA | 12313 | 1 | Y | Yes | 14 | 3 | 3 | 80 | 1 | 15 | 3 | 3 | 15 | 14 | 0 | 7 |
| 53 | No | Non-Travel | 661 | Research & Development | 1 | 4 | Medical | 1 | 1775 | 1 | Female | 60 | 2 | 4 | Manufacturing Director | 3 | Married | 12965 | 22308 | 4 | Y | Yes | 20 | 4 | 4 | 80 | 3 | 27 | 2 | 2 | 3 | 2 | 0 | 2 |
| 43 | No | Travel_Rarely | 244 | Human Resources | 2 | 3 | Life Sciences | 1 | 1778 | 2 | Male | 97 | 3 | 1 | Human Resources | 4 | Single | 3539 | 5033 | 0 | Y | No | 13 | 3 | 2 | 80 | 0 | 10 | 5 | 3 | 9 | 7 | 1 | 8 |
| 34 | No | Travel_Rarely | 511 | Sales | 3 | 2 | Life Sciences | 1 | 1779 | 4 | Female | 32 | 1 | 2 | Sales Executive | 4 | Single | 6029 | 25353 | 5 | Y | No | 12 | 3 | 1 | 80 | 0 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| 21 | Yes | Travel_Rarely | 337 | Sales | 7 | 1 | Marketing | 1 | 1780 | 2 | Male | 31 | 3 | 1 | Sales Representative | 2 | Single | 2679 | 4567 | 1 | Y | No | 13 | 3 | 2 | 80 | 0 | 1 | 3 | 3 | 1 | 0 | 1 | 0 |
| 38 | No | Travel_Rarely | 1153 | Research & Development | 6 | 2 | Other | 1 | 1782 | 4 | Female | 40 | 2 | 1 | Laboratory Technician | 3 | Married | 3702 | 16376 | 1 | Y | No | 11 | 3 | 2 | 80 | 1 | 5 | 3 | 3 | 5 | 4 | 0 | 4 |
| 22 | NA | Travel_Rarely | 1294 | Research & Development | 8 | 1 | Medical | 1 | 1783 | 3 | Female | 79 | 3 | 1 | Laboratory Technician | 1 | Married | 2398 | 15999 | 1 | Y | Yes | 17 | 3 | 3 | 80 | 0 | 1 | 6 | 3 | 1 | 0 | 0 | 0 |
| 31 | No | Travel_Rarely | 196 | Sales | 29 | 4 | Marketing | 1 | 1784 | 1 | Female | 91 | 2 | 2 | Sales Executive | 4 | Married | 5468 | 13402 | 1 | Y | No | 14 | 3 | 1 | 80 | 2 | 13 | 3 | 3 | 12 | 7 | 5 | 7 |
| 51 | NA | Travel_Rarely | 942 | Research & Development | 3 | 3 | Technical Degree | 1 | 1786 | 1 | Female | 53 | 3 | 3 | Manager | 3 | Married | 13116 | 22984 | 2 | Y | No | 11 | 3 | 4 | 80 | 0 | 15 | 2 | 3 | 2 | 2 | 2 | 2 |
| 37 | NA | Travel_Rarely | 589 | Sales | 9 | 2 | Marketing | 1 | 1787 | 2 | Male | 46 | 2 | 2 | Sales Executive | 2 | Married | 4189 | 8800 | 1 | Y | No | 14 | 3 | 1 | 80 | 2 | 5 | 2 | 3 | 5 | 2 | 0 | 3 |
| 46 | No | Travel_Rarely | 734 | Research & Development | 2 | 4 | Medical | 1 | 1789 | 3 | Male | 46 | 3 | 5 | Research Director | 4 | Divorced | 19328 | 14218 | 7 | Y | Yes | 17 | 3 | 3 | 80 | 1 | 24 | 3 | 3 | 2 | 1 | 2 | 2 |
| 36 | No | Travel_Rarely | 1383 | Research & Development | 10 | 3 | Life Sciences | 1 | 1790 | 4 | Male | 90 | 3 | 3 | Healthcare Representative | 1 | Married | 8321 | 25949 | 7 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 15 | 1 | 3 | 12 | 8 | 5 | 7 |
| 44 | NA | Travel_Frequently | 429 | Research & Development | 1 | 2 | Medical | 1 | 1792 | 3 | Male | 99 | 3 | 1 | Research Scientist | 2 | Divorced | 2342 | 11092 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 3 | 6 | 2 | 2 | 5 | 3 | 2 | 3 |
| 37 | No | Travel_Rarely | 1239 | Human Resources | 8 | 2 | Other | 1 | 1794 | 3 | Male | 89 | 3 | 2 | Human Resources | 2 | Divorced | 4071 | 12832 | 2 | Y | No | 13 | 3 | 3 | 80 | 0 | 19 | 4 | 2 | 10 | 0 | 4 | 7 |
| 35 | Yes | Travel_Rarely | 303 | Sales | 27 | 3 | Life Sciences | 1 | 1797 | 3 | Male | 84 | 3 | 2 | Sales Executive | 4 | Single | 5813 | 13492 | 1 | Y | Yes | 18 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 10 | 7 | 7 | 7 |
| 33 | No | Travel_Rarely | 867 | Research & Development | 8 | 4 | Life Sciences | 1 | 1798 | 4 | Male | 90 | 4 | 1 | Research Scientist | 1 | Married | 3143 | 6076 | 6 | Y | No | 19 | 3 | 2 | 80 | 1 | 14 | 1 | 3 | 10 | 8 | 7 | 6 |
| 28 | No | Travel_Rarely | 1181 | Research & Development | 1 | 3 | Life Sciences | 1 | 1799 | 3 | Male | 82 | 3 | 1 | Research Scientist | 4 | Married | 2044 | 5531 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 5 | 6 | 4 | 5 | 3 | 0 | 3 |
| 39 | No | Travel_Rarely | 1253 | Research & Development | 10 | 1 | Medical | 1 | 1800 | 3 | Male | 65 | 3 | 3 | Research Director | 3 | Single | 13464 | 7914 | 7 | Y | No | 21 | 4 | 3 | 80 | 0 | 9 | 3 | 3 | 4 | 3 | 2 | 2 |
| 46 | No | Non-Travel | 849 | Sales | 26 | 2 | Life Sciences | 1 | 1801 | 2 | Male | 98 | 2 | 2 | Sales Executive | 2 | Single | 7991 | 25166 | 8 | Y | No | 15 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 2 | 2 | 2 | 2 |
| 40 | No | Travel_Rarely | 616 | Research & Development | 2 | 2 | Life Sciences | 1 | 1802 | 3 | Female | 99 | 3 | 1 | Laboratory Technician | 1 | Married | 3377 | 25605 | 4 | Y | No | 17 | 3 | 4 | 80 | 1 | 7 | 5 | 2 | 4 | 3 | 0 | 2 |
| 42 | No | Travel_Rarely | 1128 | Research & Development | 13 | 3 | Medical | 1 | 1803 | 2 | Male | 95 | 4 | 2 | Healthcare Representative | 1 | Married | 5538 | 5696 | 5 | Y | No | 18 | 3 | 3 | 80 | 2 | 10 | 2 | 2 | 0 | 0 | 0 | 0 |
| 35 | No | Non-Travel | 1180 | Research & Development | 2 | 2 | Medical | 1 | 1804 | 2 | Male | 90 | 3 | 2 | Manufacturing Director | 4 | Divorced | NA | 24442 | 2 | Y | No | 14 | 3 | 3 | 80 | 1 | 15 | 6 | 3 | 7 | 7 | 1 | 7 |
| NA | No | Non-Travel | 1336 | Human Resources | 2 | 3 | Human Resources | 1 | 1805 | 1 | Male | 100 | 3 | 1 | Human Resources | 2 | Divorced | 2592 | 7129 | 5 | Y | No | 13 | 3 | 4 | 80 | 3 | 13 | 3 | 3 | 11 | 10 | 3 | 8 |
| 34 | Yes | Travel_Frequently | 234 | Research & Development | 9 | 4 | Life Sciences | 1 | 1807 | 4 | Male | 93 | 3 | 2 | Laboratory Technician | 1 | Married | 5346 | 6208 | 4 | Y | No | 17 | 3 | 3 | 80 | 1 | 11 | 3 | 2 | 7 | 1 | 0 | 7 |
| 37 | Yes | Travel_Rarely | 370 | Research & Development | 10 | 4 | Medical | 1 | 1809 | 4 | Male | 58 | 3 | 2 | Manufacturing Director | 1 | Single | 4213 | 4992 | 1 | Y | No | 15 | 3 | 2 | 80 | 0 | 10 | 4 | 1 | 10 | 3 | 0 | 8 |
| 39 | No | Travel_Frequently | 766 | Sales | 20 | 3 | Life Sciences | 1 | 1812 | 3 | Male | 83 | 3 | 2 | Sales Executive | 4 | Divorced | 4127 | 19188 | 2 | Y | No | 18 | 3 | 4 | 80 | 1 | 7 | 6 | 3 | 2 | 1 | 2 | 2 |
| 43 | No | Non-Travel | 343 | Research & Development | 9 | 3 | Life Sciences | 1 | 1813 | 1 | Male | 52 | 3 | 1 | Research Scientist | 3 | Single | 2438 | 24978 | 4 | Y | No | 13 | 3 | 3 | 80 | 0 | 7 | 2 | 2 | 3 | 2 | 1 | 2 |
| 41 | No | Travel_Rarely | 447 | Research & Development | 5 | 3 | Life Sciences | 1 | 1814 | 2 | Male | 85 | 4 | 2 | Healthcare Representative | 2 | Single | 6870 | 15530 | 3 | Y | No | 12 | 3 | 1 | 80 | 0 | 11 | 3 | 1 | 3 | 2 | 1 | 2 |
| NA | No | Travel_Rarely | 796 | Sales | 4 | 1 | Marketing | 1 | 1815 | 3 | Female | 81 | 3 | 3 | Sales Executive | 3 | Divorced | 10447 | 26458 | 0 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 23 | 3 | 4 | 22 | 14 | 13 | 5 |
| 30 | No | Travel_Rarely | 1092 | Research & Development | 10 | 3 | Medical | 1 | 1816 | 1 | Female | 64 | 3 | 3 | Manufacturing Director | 3 | Single | 9667 | 2739 | 9 | Y | No | 14 | 3 | 2 | 80 | 0 | 9 | 3 | 3 | 7 | 7 | 0 | 2 |
| 26 | Yes | Travel_Rarely | 920 | Human Resources | 20 | 2 | Medical | 1 | 1818 | 4 | Female | 69 | 3 | 1 | Human Resources | 2 | Married | 2148 | 6889 | 0 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 5 | 1 | 1 | 4 |
| NA | Yes | Travel_Rarely | 261 | Research & Development | 21 | 2 | Medical | 1 | 1821 | 4 | Female | 66 | 3 | 2 | Healthcare Representative | 2 | Married | 8926 | 10842 | 4 | Y | No | 22 | 4 | 4 | 80 | 1 | 13 | 2 | 4 | 9 | 7 | 3 | 7 |
| 40 | No | Travel_Rarely | 1194 | Research & Development | 1 | 3 | Life Sciences | 1 | 1822 | 3 | Female | 52 | 3 | 2 | Healthcare Representative | 4 | Divorced | 6513 | 9060 | 4 | Y | No | 17 | 3 | 4 | 80 | 1 | 12 | 3 | 3 | 5 | 3 | 0 | 3 |
| 34 | No | Travel_Rarely | 810 | Sales | 8 | 2 | Technical Degree | 1 | 1823 | 2 | Male | 92 | 4 | 2 | Sales Executive | 3 | Married | 6799 | 22128 | 1 | Y | No | 21 | 4 | 3 | 80 | 2 | 10 | 5 | 3 | 10 | 8 | 4 | 8 |
| 58 | No | Non-Travel | 350 | Sales | 2 | 3 | Medical | 1 | 1824 | 2 | Male | 52 | 3 | 4 | Manager | 2 | Divorced | 16291 | 22577 | 4 | Y | No | 22 | 4 | 4 | 80 | 1 | 37 | 0 | 2 | 16 | 9 | 14 | 14 |
| 35 | No | Travel_Rarely | 185 | Research & Development | 23 | 4 | Medical | 1 | 1826 | 2 | Male | 91 | 1 | 1 | Laboratory Technician | 3 | Married | 2705 | 9696 | 0 | Y | No | 16 | 3 | 2 | 80 | 1 | 6 | 2 | 4 | 5 | 4 | 0 | 3 |
| 47 | No | Travel_Rarely | 1001 | Research & Development | 4 | 3 | Life Sciences | 1 | 1827 | 3 | Female | 92 | 2 | 3 | Manufacturing Director | 2 | Divorced | 10333 | 19271 | 8 | Y | Yes | 12 | 3 | 3 | 80 | 1 | 28 | 4 | 3 | 22 | 11 | 14 | 10 |
| 40 | NA | Travel_Rarely | 750 | Research & Development | 12 | 3 | Life Sciences | 1 | 1829 | 2 | Female | 47 | 3 | 2 | Healthcare Representative | 1 | Divorced | 4448 | 10748 | 2 | Y | No | 12 | 3 | 2 | 80 | 1 | 15 | 3 | 3 | 7 | 4 | 7 | 7 |
| NA | No | Travel_Rarely | 431 | Research & Development | 7 | 4 | Medical | 1 | 1830 | 4 | Female | 68 | 3 | 2 | Research Scientist | 4 | Married | 6854 | 15696 | 4 | Y | No | 15 | 3 | 2 | 80 | 1 | 14 | 2 | 2 | 7 | 1 | 1 | 7 |
| 31 | No | Travel_Frequently | 1125 | Sales | 7 | 4 | Marketing | 1 | 1833 | 1 | Female | 68 | 3 | 3 | Sales Executive | 1 | Married | 9637 | 8277 | 2 | Y | No | 14 | 3 | 4 | 80 | 2 | 9 | 3 | 3 | 3 | 2 | 2 | 2 |
| 28 | No | Travel_Rarely | 1217 | Research & Development | 1 | 3 | Medical | 1 | 1834 | 3 | Female | 67 | 3 | 1 | Research Scientist | 1 | Married | 3591 | 12719 | 1 | Y | No | 25 | 4 | 3 | 80 | 1 | 3 | 3 | 3 | 3 | 2 | 1 | 2 |
| 38 | No | Travel_Rarely | 723 | Sales | 2 | 4 | Marketing | 1 | 1835 | 2 | Female | 77 | 1 | 2 | Sales Representative | 4 | Married | 5405 | 4244 | 2 | Y | Yes | 20 | 4 | 1 | 80 | 2 | 20 | 4 | 2 | 4 | 2 | 0 | 3 |
| NA | No | Travel_Rarely | 572 | Sales | 10 | 3 | Medical | 1 | 1836 | 3 | Male | 46 | 3 | 2 | Sales Executive | 4 | Single | 4684 | 9125 | 1 | Y | No | 13 | 3 | 1 | 80 | 0 | 5 | 4 | 3 | 5 | 3 | 1 | 2 |
| 58 | NA | Travel_Frequently | 1216 | Research & Development | 15 | 4 | Life Sciences | 1 | 1837 | 1 | Male | 87 | 3 | 4 | Research Director | 3 | Married | 15787 | 21624 | 2 | Y | Yes | 14 | 3 | 2 | 80 | 0 | 23 | 3 | 3 | 2 | 2 | 2 | 2 |
| 18 | No | Non-Travel | 1431 | Research & Development | 14 | 3 | Medical | 1 | 1839 | 2 | Female | 33 | 3 | 1 | Research Scientist | 3 | Single | 1514 | 8018 | 1 | Y | No | 16 | 3 | 3 | 80 | 0 | 0 | 4 | 1 | 0 | 0 | 0 | 0 |
| 31 | Yes | Travel_Rarely | 359 | Human Resources | 18 | 5 | Human Resources | 1 | 1842 | 4 | Male | 89 | 4 | 1 | Human Resources | 1 | Married | NA | 21495 | 0 | Y | No | 17 | 3 | 3 | 80 | 0 | 2 | 4 | 3 | 1 | 0 | 0 | 0 |
| 29 | Yes | Travel_Rarely | 350 | Human Resources | 13 | 3 | Human Resources | 1 | 1844 | 1 | Male | 56 | 2 | 1 | Human Resources | 1 | Divorced | 2335 | 3157 | 4 | Y | Yes | 15 | 3 | 4 | 80 | 3 | 4 | 3 | 3 | 2 | 2 | 2 | 0 |
| 45 | No | Non-Travel | 589 | Sales | 2 | 4 | Life Sciences | 1 | 1845 | 3 | Female | 67 | 3 | 2 | Sales Executive | 3 | Married | 5154 | 19665 | 4 | Y | No | 22 | 4 | 2 | 80 | 2 | 10 | 3 | 4 | 8 | 7 | 5 | 7 |
| 36 | NA | Travel_Rarely | 430 | Research & Development | 2 | 4 | Other | 1 | 1847 | 4 | Female | 73 | 3 | 2 | Research Scientist | 2 | Married | 6962 | 19573 | 4 | Y | Yes | 22 | 4 | 4 | 80 | 1 | 15 | 2 | 3 | 1 | 0 | 0 | 0 |
| 43 | NA | Travel_Frequently | 1422 | Sales | 2 | 4 | Life Sciences | 1 | 1849 | 1 | Male | 92 | 3 | 2 | Sales Executive | 4 | Married | 5675 | 19246 | 1 | Y | No | 20 | 4 | 3 | 80 | 1 | 7 | 5 | 3 | 7 | 7 | 7 | 7 |
| 27 | No | Travel_Frequently | 1297 | Research & Development | 5 | 2 | Life Sciences | 1 | 1850 | 4 | Female | 53 | 3 | 1 | Laboratory Technician | 4 | Single | 2379 | 19826 | 0 | Y | Yes | 14 | 3 | 3 | 80 | 0 | 6 | 3 | 2 | 5 | 4 | 0 | 2 |
| 29 | No | Travel_Frequently | 574 | Research & Development | 20 | 1 | Medical | 1 | 1852 | 4 | Male | 40 | 3 | 1 | Laboratory Technician | 4 | Married | 3812 | 7003 | 1 | Y | No | 13 | 3 | 2 | 80 | 0 | 11 | 3 | 4 | 11 | 8 | 3 | 10 |
| 32 | NA | Travel_Frequently | 1318 | Sales | 10 | 4 | Marketing | 1 | 1853 | 4 | Male | 79 | 3 | 2 | Sales Executive | 4 | Single | 4648 | 26075 | 8 | Y | No | 13 | 3 | 3 | 80 | 0 | 4 | 2 | 4 | 0 | 0 | 0 | 0 |
| 42 | No | Non-Travel | 355 | Research & Development | 10 | 4 | Technical Degree | 1 | 1854 | 3 | Male | 38 | 3 | 1 | Research Scientist | 3 | Married | 2936 | 6161 | 3 | Y | No | 22 | 4 | 2 | 80 | 2 | 10 | 1 | 2 | 6 | 3 | 3 | 3 |
| 47 | No | Travel_Rarely | 207 | Research & Development | 9 | 4 | Life Sciences | 1 | 1856 | 2 | Female | 64 | 3 | 1 | Laboratory Technician | 3 | Single | 2105 | 5411 | 4 | Y | No | 12 | 3 | 3 | 80 | 0 | 7 | 2 | 3 | 2 | 2 | 2 | 0 |
| 46 | No | Travel_Rarely | 706 | Research & Development | 2 | 2 | Life Sciences | 1 | 1857 | 4 | Male | 82 | 3 | 3 | Manufacturing Director | 4 | Divorced | 8578 | 19989 | 3 | Y | No | 14 | 3 | 3 | 80 | 1 | 12 | 4 | 2 | 9 | 8 | 4 | 7 |
| 28 | No | Non-Travel | 280 | Human Resources | 1 | 2 | Life Sciences | 1 | 1858 | 3 | Male | 43 | 3 | 1 | Human Resources | 4 | Divorced | 2706 | 10494 | 1 | Y | No | 15 | 3 | 2 | 80 | 1 | 3 | 2 | 3 | 3 | 2 | 2 | 2 |
| 29 | No | Travel_Rarely | 726 | Research & Development | 29 | 1 | Life Sciences | 1 | 1859 | 4 | Male | 93 | 1 | 2 | Healthcare Representative | 3 | Divorced | 6384 | 21143 | 8 | Y | No | 17 | 3 | 4 | 80 | 2 | 11 | 3 | 3 | 7 | 0 | 1 | 6 |
| 42 | No | Travel_Rarely | 1142 | Research & Development | 8 | 3 | Life Sciences | 1 | 1860 | 4 | Male | 81 | 3 | 1 | Laboratory Technician | 3 | Single | 3968 | 13624 | 4 | Y | No | 13 | 3 | 4 | 80 | 0 | 8 | 3 | 3 | 0 | 0 | 0 | 0 |
| 32 | Yes | Travel_Rarely | 414 | Sales | 2 | 4 | Marketing | 1 | 1862 | 3 | Male | 82 | 2 | 2 | Sales Executive | 2 | Single | 9907 | 26186 | 7 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 7 | 3 | 2 | 2 | 2 | 2 | 2 |
| 46 | No | Travel_Rarely | 1319 | Sales | 3 | 3 | Technical Degree | 1 | 1863 | 1 | Female | 45 | 4 | 4 | Sales Executive | 1 | Divorced | 13225 | 7739 | 2 | Y | No | 12 | 3 | 4 | 80 | 1 | 25 | 5 | 3 | 19 | 17 | 2 | 8 |
| 27 | No | Travel_Rarely | 728 | Sales | 23 | 1 | Medical | 1 | 1864 | 2 | Female | 36 | 2 | 2 | Sales Representative | 3 | Married | 3540 | 7018 | 1 | Y | No | 21 | 4 | 4 | 80 | 1 | 9 | 5 | 3 | 9 | 8 | 5 | 8 |
| 29 | No | Travel_Rarely | 352 | Human Resources | 6 | 1 | Medical | 1 | 1865 | 4 | Male | 87 | 2 | 1 | Human Resources | 2 | Married | 2804 | 15434 | 1 | Y | No | 11 | 3 | 4 | 80 | 0 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 43 | No | Travel_Rarely | 823 | Research & Development | 6 | 3 | Medical | 1 | 1866 | 1 | Female | 81 | 2 | 5 | Manager | 3 | Married | 19392 | 22539 | 7 | Y | No | 13 | 3 | 4 | 80 | 0 | 21 | 2 | 3 | 16 | 12 | 6 | 14 |
| 48 | No | Travel_Rarely | 1224 | Research & Development | 10 | 3 | Life Sciences | 1 | 1867 | 4 | Male | 91 | 2 | 5 | Research Director | 2 | Married | 19665 | 13583 | 4 | Y | No | 12 | 3 | 4 | 80 | 0 | 29 | 3 | 3 | 22 | 10 | 12 | 9 |
| 29 | Yes | Travel_Frequently | 459 | Research & Development | 24 | 2 | Life Sciences | 1 | 1868 | 4 | Male | 73 | 2 | 1 | Research Scientist | 4 | Single | 2439 | 14753 | 1 | Y | Yes | 24 | 4 | 2 | 80 | 0 | 1 | 3 | 2 | 1 | 0 | 1 | 0 |
| 46 | Yes | Travel_Rarely | 1254 | Sales | 10 | 3 | Life Sciences | 1 | 1869 | 3 | Female | 64 | 3 | 3 | Sales Executive | 2 | Married | 7314 | 14011 | 5 | Y | No | 21 | 4 | 3 | 80 | 3 | 14 | 2 | 3 | 8 | 7 | 0 | 7 |
| 27 | No | Travel_Frequently | 1131 | Research & Development | 15 | 3 | Life Sciences | 1 | 1870 | 4 | Female | 77 | 2 | 1 | Research Scientist | 1 | Married | NA | 23844 | 0 | Y | No | 19 | 3 | 4 | 80 | 1 | 8 | 2 | 2 | 7 | 6 | 7 | 3 |
| 39 | No | Travel_Rarely | 835 | Research & Development | 19 | 4 | Other | 1 | 1871 | 4 | Male | 41 | 3 | 2 | Research Scientist | 4 | Divorced | 3902 | 5141 | 8 | Y | No | 14 | 3 | 2 | 80 | 3 | 7 | 2 | 3 | 2 | 2 | 2 | 2 |
| 55 | No | Travel_Rarely | 836 | Research & Development | 2 | 4 | Technical Degree | 1 | 1873 | 2 | Male | 98 | 2 | 1 | Research Scientist | 4 | Married | 2662 | 7975 | 8 | Y | No | 20 | 4 | 2 | 80 | 1 | 19 | 2 | 4 | 5 | 2 | 0 | 4 |
| 28 | No | Travel_Rarely | 1172 | Sales | 3 | 3 | Medical | 1 | 1875 | 2 | Female | 78 | 3 | 1 | Sales Representative | 2 | Married | 2856 | 3692 | 1 | Y | No | 19 | 3 | 4 | 80 | 1 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 30 | Yes | Travel_Rarely | 945 | Sales | 9 | 3 | Medical | 1 | 1876 | 2 | Male | 89 | 3 | 1 | Sales Representative | 4 | Single | 1081 | 16019 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 1 | 3 | 2 | 1 | 0 | 0 | 0 |
| 22 | Yes | Travel_Rarely | 391 | Research & Development | 7 | 1 | Life Sciences | 1 | 1878 | 4 | Male | 75 | 3 | 1 | Research Scientist | 2 | Single | 2472 | 26092 | 1 | Y | Yes | 23 | 4 | 1 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 36 | No | Travel_Rarely | 1266 | Sales | 10 | 4 | Technical Degree | 1 | 1880 | 2 | Female | 63 | 2 | 2 | Sales Executive | 3 | Married | 5673 | 6060 | 1 | Y | Yes | 13 | 3 | 1 | 80 | 1 | 10 | 4 | 3 | 10 | 9 | 1 | 7 |
| 31 | No | Travel_Rarely | 311 | Research & Development | 20 | 3 | Life Sciences | 1 | 1881 | 2 | Male | 89 | 3 | 2 | Laboratory Technician | 3 | Divorced | 4197 | 18624 | 1 | Y | No | 11 | 3 | 1 | 80 | 1 | 10 | 2 | 3 | 10 | 8 | 0 | 2 |
| 34 | No | Travel_Rarely | 1480 | Sales | 4 | 3 | Life Sciences | 1 | 1882 | 3 | Male | 64 | 3 | 3 | Sales Executive | 4 | Married | 9713 | 24444 | 2 | Y | Yes | 13 | 3 | 4 | 80 | 3 | 9 | 3 | 3 | 5 | 3 | 1 | 0 |
| 29 | NA | Travel_Rarely | 592 | Research & Development | 7 | 3 | Life Sciences | 1 | 1883 | 4 | Male | 59 | 3 | 1 | Laboratory Technician | 1 | Single | 2062 | 19384 | 3 | Y | No | 14 | 3 | 2 | 80 | 0 | 11 | 2 | 3 | 3 | 2 | 1 | 2 |
| 37 | No | Travel_Rarely | 783 | Research & Development | 7 | 4 | Medical | 1 | 1885 | 4 | Male | 78 | 3 | 2 | Research Scientist | 1 | Married | 4284 | 13588 | 5 | Y | Yes | 22 | 4 | 3 | 80 | 1 | 16 | 2 | 3 | 5 | 3 | 0 | 4 |
| 35 | No | Travel_Rarely | 219 | Research & Development | 16 | 2 | Other | 1 | 1886 | 4 | Female | 44 | 2 | 2 | Manufacturing Director | 2 | Married | 4788 | 25388 | 0 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 4 | 2 | 3 | 3 | 2 | 0 | 2 |
| 45 | No | Travel_Rarely | 556 | Research & Development | 25 | 2 | Life Sciences | 1 | 1888 | 2 | Female | 93 | 2 | 2 | Manufacturing Director | 4 | Married | 5906 | 23888 | 0 | Y | No | 13 | 3 | 4 | 80 | 2 | 10 | 2 | 2 | 9 | 8 | 3 | 8 |
| 36 | No | Travel_Frequently | 1213 | Human Resources | 2 | 1 | Human Resources | 1 | 1890 | 2 | Male | 94 | 2 | 2 | Human Resources | 4 | Single | NA | 4223 | 1 | Y | No | 21 | 4 | 4 | 80 | 0 | 10 | 2 | 2 | 10 | 1 | 0 | 8 |
| 40 | No | Travel_Rarely | 1137 | Research & Development | 1 | 4 | Life Sciences | 1 | 1892 | 1 | Male | 98 | 3 | 4 | Manager | 1 | Divorced | 16823 | 18991 | 2 | Y | No | 11 | 3 | 1 | 80 | 1 | 22 | 3 | 3 | 19 | 7 | 11 | 16 |
| 26 | No | Travel_Rarely | 482 | Research & Development | 1 | 2 | Life Sciences | 1 | 1893 | 2 | Female | 90 | 2 | 1 | Research Scientist | 3 | Married | NA | 14908 | 1 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 1 | 3 | 2 | 1 | 0 | 1 | 0 |
| 27 | No | Travel_Rarely | 511 | Sales | 2 | 2 | Medical | 1 | 1898 | 1 | Female | 89 | 4 | 2 | Sales Executive | 3 | Single | 6500 | 26997 | 0 | Y | No | 14 | 3 | 2 | 80 | 0 | 9 | 5 | 2 | 8 | 7 | 0 | 7 |
| 48 | NA | Travel_Frequently | 117 | Research & Development | 22 | 3 | Medical | 1 | 1900 | 4 | Female | 58 | 3 | 4 | Manager | 4 | Divorced | NA | 2437 | 3 | Y | No | 11 | 3 | 2 | 80 | 1 | 24 | 3 | 3 | 22 | 17 | 4 | 7 |
| 44 | No | Travel_Rarely | 170 | Research & Development | 1 | 4 | Life Sciences | 1 | 1903 | 2 | Male | 78 | 4 | 2 | Healthcare Representative | 1 | Married | 5033 | 9364 | 2 | Y | No | 15 | 3 | 4 | 80 | 1 | 10 | 5 | 3 | 2 | 0 | 2 | 2 |
| 34 | Yes | Non-Travel | 967 | Research & Development | 16 | 4 | Technical Degree | 1 | 1905 | 4 | Male | 85 | 1 | 1 | Research Scientist | 1 | Married | 2307 | 14460 | 1 | Y | Yes | 23 | 4 | 2 | 80 | 1 | 5 | 2 | 3 | 5 | 2 | 3 | 0 |
| 56 | Yes | Travel_Rarely | 1162 | Research & Development | 24 | 2 | Life Sciences | 1 | 1907 | 1 | Male | 97 | 3 | 1 | Laboratory Technician | 4 | Single | 2587 | 10261 | 1 | Y | No | 16 | 3 | 4 | 80 | 0 | 5 | 3 | 3 | 4 | 2 | 1 | 0 |
| 36 | NA | Travel_Rarely | 335 | Sales | 17 | 2 | Marketing | 1 | 1908 | 3 | Male | 33 | 2 | 2 | Sales Executive | 2 | Married | 5507 | 16822 | 2 | Y | No | 16 | 3 | 3 | 80 | 2 | 12 | 1 | 1 | 4 | 2 | 1 | 3 |
| 41 | No | Travel_Rarely | 337 | Sales | 8 | 3 | Marketing | 1 | 1909 | 3 | Female | 54 | 3 | 2 | Sales Executive | 2 | Married | 4393 | 26841 | 5 | Y | No | 21 | 4 | 3 | 80 | 1 | 14 | 3 | 3 | 5 | 4 | 1 | 4 |
| 42 | No | Travel_Rarely | 1396 | Research & Development | 6 | 3 | Medical | 1 | 1911 | 3 | Male | 83 | 3 | 3 | Research Director | 1 | Married | 13348 | 14842 | 9 | Y | No | 13 | 3 | 2 | 80 | 1 | 18 | 3 | 4 | 13 | 7 | 5 | 7 |
| 31 | No | Travel_Rarely | 1079 | Sales | 10 | 2 | Medical | 1 | 1912 | 3 | Female | 86 | 3 | 2 | Sales Executive | 4 | Divorced | 6583 | 20115 | 2 | Y | Yes | 11 | 3 | 4 | 80 | 1 | 8 | 2 | 3 | 5 | 2 | 1 | 4 |
| 34 | No | Travel_Rarely | 735 | Sales | 3 | 1 | Medical | 1 | 1915 | 4 | Female | 75 | 2 | 2 | Sales Executive | 4 | Married | NA | 16495 | 3 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 9 | 3 | 2 | 4 | 2 | 0 | 1 |
| 31 | NA | Travel_Rarely | 471 | Research & Development | 4 | 3 | Medical | 1 | 1916 | 1 | Female | 62 | 4 | 1 | Laboratory Technician | 3 | Divorced | 3978 | 16031 | 8 | Y | No | 12 | 3 | 2 | 80 | 1 | 4 | 0 | 2 | 2 | 2 | 2 | 2 |
| 26 | No | Travel_Frequently | 1096 | Research & Development | 6 | 3 | Other | 1 | 1918 | 3 | Male | 61 | 4 | 1 | Laboratory Technician | 4 | Married | 2544 | 7102 | 0 | Y | No | 18 | 3 | 1 | 80 | 1 | 8 | 3 | 3 | 7 | 7 | 7 | 7 |
| 45 | No | Travel_Frequently | 1297 | Research & Development | 1 | 4 | Medical | 1 | 1922 | 2 | Male | 44 | 3 | 2 | Healthcare Representative | 3 | Single | 5399 | 14511 | 4 | Y | No | 12 | 3 | 3 | 80 | 0 | 12 | 3 | 3 | 4 | 2 | 0 | 3 |
| NA | No | Travel_Rarely | 217 | Sales | 10 | 4 | Marketing | 1 | 1924 | 2 | Male | 43 | 3 | 2 | Sales Executive | 3 | Single | 5487 | 10410 | 1 | Y | No | 14 | 3 | 2 | 80 | 0 | 10 | 2 | 2 | 10 | 4 | 0 | 9 |
| 28 | No | Travel_Frequently | 783 | Sales | 1 | 2 | Life Sciences | 1 | 1927 | 3 | Male | 42 | 2 | 2 | Sales Executive | 4 | Married | 6834 | 19255 | 1 | Y | Yes | 12 | 3 | 3 | 80 | 1 | 7 | 2 | 3 | 7 | 7 | 0 | 7 |
| 29 | Yes | Travel_Frequently | 746 | Sales | 24 | 3 | Technical Degree | 1 | 1928 | 3 | Male | 45 | 4 | 1 | Sales Representative | 1 | Single | 1091 | 10642 | 1 | Y | No | 17 | 3 | 4 | 80 | 0 | 1 | 3 | 3 | 1 | 0 | 0 | 0 |
| 39 | No | Non-Travel | 1251 | Sales | 21 | 4 | Life Sciences | 1 | 1929 | 1 | Female | 32 | 1 | 2 | Sales Executive | 3 | Married | 5736 | 3987 | 6 | Y | No | 19 | 3 | 3 | 80 | 1 | 10 | 1 | 3 | 3 | 2 | 1 | 2 |
| 27 | NA | Travel_Rarely | 1354 | Research & Development | 2 | 4 | Technical Degree | 1 | 1931 | 2 | Male | 41 | 3 | 1 | Research Scientist | 2 | Married | 2226 | 6073 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 6 | 3 | 2 | 5 | 3 | 1 | 2 |
| 34 | No | Travel_Frequently | 735 | Research & Development | 22 | 4 | Other | 1 | 1932 | 3 | Male | 86 | 2 | 2 | Research Scientist | 4 | Married | NA | 26496 | 1 | Y | Yes | 15 | 3 | 2 | 80 | 0 | 16 | 3 | 3 | 15 | 10 | 6 | 11 |
| 28 | Yes | Travel_Rarely | 1475 | Sales | 13 | 2 | Marketing | 1 | 1933 | 4 | Female | 84 | 3 | 2 | Sales Executive | 3 | Single | 9854 | 23352 | 3 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 6 | 0 | 3 | 2 | 0 | 2 | 2 |
| 47 | No | Non-Travel | 1169 | Research & Development | 14 | 4 | Technical Degree | 1 | 1934 | 3 | Male | 64 | 3 | 2 | Research Scientist | 2 | Married | 5467 | 2125 | 8 | Y | No | 18 | 3 | 3 | 80 | 1 | 16 | 4 | 4 | 8 | 7 | 1 | 7 |
| 56 | No | Travel_Rarely | 1443 | Sales | 11 | 5 | Marketing | 1 | 1935 | 4 | Female | 89 | 2 | 2 | Sales Executive | 1 | Married | 5380 | 20328 | 4 | Y | No | 16 | 3 | 3 | 80 | 1 | 6 | 3 | 3 | 0 | 0 | 0 | 0 |
| 39 | No | Travel_Rarely | 867 | Research & Development | 9 | 2 | Medical | 1 | 1936 | 1 | Male | 87 | 3 | 2 | Manufacturing Director | 1 | Married | 5151 | 12315 | 1 | Y | No | 25 | 4 | 4 | 80 | 1 | 10 | 3 | 3 | 10 | 0 | 7 | 9 |
| 38 | NA | Travel_Frequently | 1394 | Research & Development | 8 | 3 | Medical | 1 | 1937 | 4 | Female | 58 | 2 | 2 | Research Scientist | 2 | Divorced | 2133 | 18115 | 1 | Y | Yes | 16 | 3 | 3 | 80 | 1 | 20 | 3 | 3 | 20 | 11 | 0 | 7 |
| 58 | No | Travel_Rarely | 605 | Sales | 21 | 3 | Life Sciences | 1 | 1938 | 4 | Female | 72 | 3 | 4 | Manager | 4 | Married | 17875 | 11761 | 4 | Y | Yes | 13 | 3 | 3 | 80 | 1 | 29 | 2 | 2 | 1 | 0 | 0 | 0 |
| 32 | Yes | Travel_Frequently | 238 | Research & Development | 5 | 2 | Life Sciences | 1 | 1939 | 1 | Female | 47 | 4 | 1 | Research Scientist | 3 | Single | 2432 | 15318 | 3 | Y | Yes | 14 | 3 | 1 | 80 | 0 | 8 | 2 | 3 | 4 | 1 | 0 | 3 |
| 38 | No | Travel_Rarely | 1206 | Research & Development | 9 | 2 | Life Sciences | 1 | 1940 | 2 | Male | 71 | 3 | 1 | Research Scientist | 4 | Divorced | 4771 | 14293 | 2 | Y | No | 19 | 3 | 4 | 80 | 2 | 10 | 0 | 4 | 5 | 2 | 0 | 3 |
| 49 | No | Travel_Frequently | 1064 | Research & Development | 2 | 1 | Life Sciences | 1 | 1941 | 2 | Male | 42 | 3 | 5 | Research Director | 4 | Married | NA | 13738 | 3 | Y | No | 15 | 3 | 4 | 80 | 0 | 28 | 3 | 3 | 5 | 4 | 4 | 3 |
| NA | NA | Travel_Rarely | 419 | Sales | 12 | 4 | Marketing | 1 | 1943 | 2 | Male | 77 | 3 | 2 | Sales Executive | 4 | Divorced | 5087 | 2900 | 3 | Y | Yes | 12 | 3 | 3 | 80 | 2 | 14 | 4 | 3 | 0 | 0 | 0 | 0 |
| 27 | Yes | Travel_Frequently | 1337 | Human Resources | 22 | 3 | Human Resources | 1 | 1944 | 1 | Female | 58 | 2 | 1 | Human Resources | 2 | Married | 2863 | 19555 | 1 | Y | No | 12 | 3 | 1 | 80 | 0 | 1 | 2 | 3 | 1 | 0 | 0 | 0 |
| 35 | No | Travel_Rarely | 682 | Sales | 18 | 4 | Medical | 1 | 1945 | 2 | Male | 71 | 3 | 2 | Sales Executive | 1 | Married | 5561 | 15975 | 0 | Y | No | 16 | 3 | 4 | 80 | 1 | 6 | 2 | 1 | 5 | 3 | 0 | 4 |
| 28 | No | Non-Travel | 1103 | Research & Development | 16 | 3 | Medical | 1 | 1947 | 3 | Male | 49 | 3 | 1 | Research Scientist | 3 | Single | 2144 | 2122 | 1 | Y | No | 14 | 3 | 3 | 80 | 0 | 5 | 3 | 2 | 5 | 3 | 1 | 4 |
| 31 | No | Non-Travel | 976 | Research & Development | 3 | 2 | Medical | 1 | 1948 | 3 | Male | 48 | 3 | 1 | Research Scientist | 1 | Divorced | 3065 | 3995 | 1 | Y | Yes | 13 | 3 | 4 | 80 | 1 | 4 | 3 | 4 | 4 | 2 | 2 | 3 |
| 36 | No | Non-Travel | 1351 | Research & Development | 9 | 4 | Life Sciences | 1 | 1949 | 1 | Male | 66 | 4 | 1 | Laboratory Technician | 2 | Married | 2810 | 9238 | 1 | Y | No | 22 | 4 | 2 | 80 | 0 | 5 | 3 | 3 | 5 | 4 | 0 | 2 |
| NA | NA | Travel_Rarely | 937 | Sales | 1 | 3 | Marketing | 1 | 1950 | 1 | Male | 32 | 3 | 3 | Sales Executive | 4 | Single | 9888 | 6770 | 1 | Y | No | 21 | 4 | 1 | 80 | 0 | 14 | 3 | 2 | 14 | 8 | 2 | 1 |
| 34 | No | Travel_Rarely | 1239 | Sales | 13 | 4 | Medical | 1 | 1951 | 4 | Male | 39 | 3 | 3 | Sales Executive | 3 | Divorced | 8628 | 22914 | 1 | Y | No | 18 | 3 | 3 | 80 | 1 | 9 | 2 | 2 | 8 | 7 | 1 | 1 |
| NA | No | Travel_Rarely | 157 | Research & Development | 1 | 3 | Medical | 1 | 1952 | 3 | Male | 95 | 3 | 1 | Laboratory Technician | 1 | Single | NA | 20006 | 0 | Y | No | 13 | 3 | 4 | 80 | 0 | 8 | 6 | 2 | 7 | 7 | 7 | 6 |
| 29 | No | Travel_Rarely | 136 | Research & Development | 1 | 3 | Life Sciences | 1 | 1954 | 1 | Male | 89 | 3 | 2 | Healthcare Representative | 1 | Married | 5373 | 6225 | 0 | Y | No | 12 | 3 | 1 | 80 | 1 | 6 | 5 | 2 | 5 | 3 | 0 | 2 |
| 32 | No | Non-Travel | 1146 | Research & Development | 15 | 4 | Medical | 1 | 1955 | 3 | Female | 34 | 3 | 2 | Healthcare Representative | 4 | Divorced | NA | 16542 | 5 | Y | No | 18 | 3 | 2 | 80 | 1 | 9 | 6 | 3 | 5 | 1 | 1 | 2 |
| 31 | No | Travel_Frequently | 1125 | Research & Development | 1 | 3 | Life Sciences | 1 | 1956 | 4 | Male | 48 | 1 | 2 | Research Scientist | 1 | Married | 5003 | 5771 | 1 | Y | No | 21 | 4 | 2 | 80 | 0 | 10 | 6 | 3 | 10 | 8 | 8 | 7 |
| 28 | Yes | Travel_Rarely | 1404 | Research & Development | 17 | 3 | Technical Degree | 1 | 1960 | 3 | Male | 32 | 2 | 1 | Laboratory Technician | 4 | Divorced | 2367 | 18779 | 5 | Y | No | 12 | 3 | 1 | 80 | 1 | 6 | 2 | 2 | 4 | 1 | 0 | 3 |
| 38 | No | Travel_Rarely | 1404 | Sales | 1 | 3 | Life Sciences | 1 | 1961 | 1 | Male | 59 | 2 | 1 | Sales Representative | 1 | Single | 2858 | 11473 | 4 | Y | No | 14 | 3 | 1 | 80 | 0 | 20 | 3 | 2 | 1 | 0 | 0 | 0 |
| 35 | No | Travel_Rarely | 1224 | Sales | 7 | 4 | Life Sciences | 1 | 1962 | 3 | Female | 55 | 3 | 2 | Sales Executive | 4 | Married | 5204 | 13586 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 10 | 2 | 3 | 10 | 8 | 0 | 9 |
| 27 | No | Travel_Rarely | 954 | Sales | 9 | 3 | Marketing | 1 | 1965 | 4 | Male | 44 | 3 | 2 | Sales Executive | 4 | Single | 4105 | 5099 | 1 | Y | No | 14 | 3 | 1 | 80 | 0 | 7 | 5 | 3 | 7 | 7 | 0 | 7 |
| 32 | No | Travel_Rarely | 1373 | Research & Development | 5 | 4 | Life Sciences | 1 | 1966 | 4 | Male | 56 | 2 | 2 | Manufacturing Director | 4 | Single | 9679 | 10138 | 8 | Y | No | 24 | 4 | 2 | 80 | 0 | 8 | 1 | 3 | 1 | 0 | 0 | 0 |
| 31 | Yes | Travel_Frequently | 754 | Sales | 26 | 4 | Marketing | 1 | 1967 | 1 | Male | 63 | 3 | 2 | Sales Executive | 4 | Married | 5617 | 21075 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 0 | 10 | 4 | 3 | 10 | 7 | 0 | 8 |
| NA | Yes | Travel_Rarely | 1168 | Sales | 24 | 4 | Life Sciences | 1 | 1968 | 1 | Male | 66 | 3 | 3 | Sales Executive | 1 | Single | 10448 | 5843 | 6 | Y | Yes | 13 | 3 | 2 | 80 | 0 | 15 | 2 | 2 | 2 | 2 | 2 | 2 |
| 54 | No | Travel_Rarely | 155 | Research & Development | 9 | 2 | Life Sciences | 1 | 1969 | 1 | Female | 67 | 3 | 2 | Research Scientist | 3 | Married | 2897 | 22474 | 3 | Y | No | 11 | 3 | 3 | 80 | 2 | 9 | 6 | 2 | 4 | 3 | 2 | 3 |
| 33 | No | Travel_Frequently | 1303 | Research & Development | 7 | 2 | Life Sciences | 1 | 1970 | 4 | Male | 36 | 3 | 2 | Healthcare Representative | 3 | Divorced | 5968 | 18079 | 1 | Y | No | 20 | 4 | 3 | 80 | 3 | 9 | 2 | 3 | 9 | 7 | 2 | 8 |
| 43 | No | Travel_Rarely | 574 | Research & Development | 11 | 3 | Life Sciences | 1 | 1971 | 1 | Male | 30 | 3 | 3 | Healthcare Representative | 3 | Married | 7510 | 16873 | 1 | Y | No | 17 | 3 | 2 | 80 | 1 | 10 | 1 | 3 | 10 | 9 | 0 | 9 |
| 38 | No | Travel_Frequently | 1444 | Human Resources | 1 | 4 | Other | 1 | 1972 | 4 | Male | 88 | 3 | 1 | Human Resources | 2 | Married | 2991 | 5224 | 0 | Y | Yes | 11 | 3 | 2 | 80 | 1 | 7 | 2 | 3 | 6 | 2 | 1 | 2 |
| 55 | No | Travel_Rarely | 189 | Human Resources | 26 | 4 | Human Resources | 1 | 1973 | 3 | Male | 71 | 4 | 5 | Manager | 2 | Married | 19636 | 25811 | 4 | Y | Yes | 18 | 3 | 1 | 80 | 1 | 35 | 0 | 3 | 10 | 9 | 1 | 4 |
| 31 | NA | Travel_Rarely | 1276 | Research & Development | 2 | 1 | Medical | 1 | 1974 | 4 | Female | 59 | 1 | 1 | Laboratory Technician | 4 | Divorced | 1129 | 17536 | 1 | Y | Yes | 11 | 3 | 3 | 80 | 3 | 1 | 4 | 3 | 1 | 0 | 0 | 0 |
| 39 | No | Travel_Rarely | 119 | Sales | 15 | 4 | Marketing | 1 | 1975 | 2 | Male | 77 | 3 | 4 | Sales Executive | 1 | Single | 13341 | 25098 | 0 | Y | No | 12 | 3 | 1 | 80 | 0 | 21 | 3 | 3 | 20 | 8 | 11 | 10 |
| 42 | NA | Non-Travel | 335 | Research & Development | 23 | 2 | Life Sciences | 1 | 1976 | 4 | Male | 37 | 2 | 2 | Research Scientist | 3 | Single | 4332 | 14811 | 1 | Y | No | 12 | 3 | 4 | 80 | 0 | 20 | 2 | 3 | 20 | 9 | 3 | 7 |
| 31 | No | Non-Travel | 697 | Research & Development | 10 | 3 | Medical | 1 | 1979 | 3 | Female | 40 | 3 | 3 | Research Director | 3 | Married | 11031 | 26862 | 4 | Y | No | 20 | 4 | 3 | 80 | 1 | 13 | 2 | 4 | 11 | 7 | 4 | 8 |
| 54 | No | Travel_Rarely | 157 | Research & Development | 10 | 3 | Medical | 1 | 1980 | 3 | Female | 77 | 3 | 2 | Manufacturing Director | 1 | Single | 4440 | 25198 | 6 | Y | Yes | 19 | 3 | 4 | 80 | 0 | 9 | 3 | 3 | 5 | 2 | 1 | 4 |
| 24 | No | Travel_Rarely | 771 | Research & Development | 1 | 2 | Life Sciences | 1 | 1981 | 2 | Male | 45 | 2 | 2 | Healthcare Representative | 3 | Single | 4617 | 14120 | 1 | Y | No | 12 | 3 | 2 | 80 | 0 | 4 | 2 | 2 | 4 | 3 | 1 | 2 |
| 23 | NA | Travel_Rarely | 571 | Research & Development | 12 | 2 | Other | 1 | 1982 | 4 | Male | 78 | 3 | 1 | Laboratory Technician | 4 | Single | 2647 | 13672 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 5 | 6 | 4 | 5 | 2 | 1 | 4 |
| 40 | No | Travel_Frequently | 692 | Research & Development | 11 | 3 | Technical Degree | 1 | 1985 | 4 | Female | 73 | 3 | 2 | Laboratory Technician | 3 | Married | 6323 | 26849 | 1 | Y | No | 11 | 3 | 1 | 80 | 1 | 10 | 2 | 4 | 10 | 9 | 9 | 4 |
| 40 | No | Travel_Rarely | 444 | Sales | 2 | 2 | Marketing | 1 | 1986 | 2 | Female | 92 | 3 | 2 | Sales Executive | 2 | Married | NA | 4258 | 3 | Y | No | 14 | 3 | 3 | 80 | 1 | 15 | 4 | 3 | 11 | 8 | 5 | 10 |
| 25 | No | Travel_Rarely | 309 | Human Resources | 2 | 3 | Human Resources | 1 | 1987 | 3 | Female | 82 | 3 | 1 | Human Resources | 2 | Married | 2187 | 19655 | 4 | Y | No | 14 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 2 | 0 | 1 | 2 |
| 30 | No | Travel_Rarely | 911 | Research & Development | 1 | 2 | Medical | 1 | 1989 | 4 | Male | 76 | 3 | 1 | Laboratory Technician | 2 | Married | 3748 | 4077 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 12 | 6 | 2 | 12 | 8 | 1 | 7 |
| NA | No | Travel_Rarely | 977 | Research & Development | 2 | 1 | Other | 1 | 1992 | 4 | Male | 57 | 3 | 1 | Laboratory Technician | 3 | Divorced | 3977 | 7298 | 6 | Y | Yes | 19 | 3 | 3 | 80 | 1 | 7 | 2 | 2 | 2 | 2 | 0 | 2 |
| 47 | No | Travel_Rarely | 1180 | Research & Development | 25 | 3 | Medical | 1 | 1993 | 1 | Male | 84 | 3 | 3 | Healthcare Representative | 3 | Single | 8633 | 13084 | 2 | Y | No | 23 | 4 | 2 | 80 | 0 | 25 | 3 | 3 | 17 | 14 | 12 | 11 |
| 33 | No | Non-Travel | 1313 | Research & Development | 1 | 2 | Medical | 1 | 1994 | 2 | Male | 59 | 2 | 1 | Laboratory Technician | 3 | Divorced | 2008 | 20439 | 1 | Y | No | 12 | 3 | 3 | 80 | 3 | 1 | 2 | 2 | 1 | 1 | 0 | 0 |
| 38 | No | Travel_Rarely | 1321 | Sales | 1 | 4 | Life Sciences | 1 | 1995 | 4 | Male | 86 | 3 | 2 | Sales Executive | 2 | Married | 4440 | 7636 | 0 | Y | No | 15 | 3 | 1 | 80 | 2 | 16 | 3 | 3 | 15 | 13 | 5 | 8 |
| 31 | NA | Travel_Rarely | 1154 | Sales | 2 | 2 | Life Sciences | 1 | 1996 | 1 | Male | 54 | 3 | 1 | Sales Representative | 3 | Married | 3067 | 6393 | 0 | Y | No | 19 | 3 | 3 | 80 | 1 | 3 | 1 | 3 | 2 | 2 | 1 | 2 |
| 38 | No | Travel_Frequently | 508 | Research & Development | 6 | 4 | Life Sciences | 1 | 1997 | 1 | Male | 72 | 2 | 2 | Manufacturing Director | 3 | Married | 5321 | 14284 | 2 | Y | No | 11 | 3 | 4 | 80 | 1 | 10 | 1 | 3 | 8 | 3 | 7 | 7 |
| 42 | No | Travel_Rarely | 557 | Research & Development | 18 | 4 | Life Sciences | 1 | 1998 | 4 | Male | 35 | 3 | 2 | Research Scientist | 1 | Divorced | 5410 | 11189 | 6 | Y | Yes | 17 | 3 | 3 | 80 | 1 | 9 | 3 | 2 | 4 | 3 | 1 | 2 |
| 41 | No | Travel_Rarely | 642 | Research & Development | 1 | 3 | Life Sciences | 1 | 1999 | 4 | Male | 76 | 3 | 1 | Research Scientist | 4 | Married | 2782 | 21412 | 3 | Y | No | 22 | 4 | 1 | 80 | 1 | 12 | 3 | 3 | 5 | 3 | 1 | 0 |
| 47 | No | Non-Travel | 1162 | Research & Development | 1 | 1 | Medical | 1 | 2000 | 3 | Female | 98 | 3 | 3 | Research Director | 2 | Married | NA | 17231 | 0 | Y | No | 18 | 3 | 1 | 80 | 2 | 14 | 3 | 1 | 13 | 8 | 5 | 12 |
| NA | No | Travel_Rarely | 1490 | Research & Development | 11 | 4 | Medical | 1 | 2003 | 4 | Male | 43 | 3 | 1 | Laboratory Technician | 3 | Married | 2660 | 20232 | 7 | Y | Yes | 11 | 3 | 3 | 80 | 1 | 5 | 3 | 3 | 2 | 2 | 2 | 2 |
| 22 | No | Travel_Rarely | 581 | Research & Development | 1 | 2 | Life Sciences | 1 | 2007 | 4 | Male | 63 | 3 | 1 | Research Scientist | 3 | Single | 3375 | 17624 | 0 | Y | No | 12 | 3 | 4 | 80 | 0 | 4 | 2 | 4 | 3 | 2 | 1 | 2 |
| 35 | No | Travel_Rarely | 1395 | Research & Development | 9 | 4 | Medical | 1 | 2008 | 2 | Male | 48 | 3 | 2 | Research Scientist | 3 | Single | 5098 | 18698 | 1 | Y | No | 19 | 3 | 2 | 80 | 0 | 10 | 5 | 3 | 10 | 7 | 0 | 8 |
| 33 | No | Travel_Rarely | 501 | Research & Development | 15 | 2 | Medical | 1 | 2009 | 2 | Female | 95 | 3 | 2 | Healthcare Representative | 4 | Married | NA | 21653 | 0 | Y | Yes | 13 | 3 | 1 | 80 | 1 | 10 | 6 | 3 | 9 | 7 | 8 | 1 |
| 32 | No | Travel_Rarely | 267 | Research & Development | 29 | 4 | Life Sciences | 1 | 2010 | 3 | Female | 49 | 2 | 1 | Laboratory Technician | 2 | Single | 2837 | 15919 | 1 | Y | No | 13 | 3 | 3 | 80 | 0 | 6 | 3 | 3 | 6 | 2 | 4 | 1 |
| 40 | No | Travel_Rarely | 543 | Research & Development | 1 | 4 | Life Sciences | 1 | 2012 | 1 | Male | 83 | 3 | 1 | Laboratory Technician | 4 | Married | 2406 | 4060 | 8 | Y | No | 19 | 3 | 3 | 80 | 2 | 8 | 3 | 2 | 1 | 0 | 0 | 0 |
| 32 | No | Travel_Rarely | 234 | Sales | 1 | 4 | Medical | 1 | 2013 | 2 | Male | 68 | 2 | 1 | Sales Representative | 2 | Married | 2269 | 18024 | 0 | Y | No | 14 | 3 | 2 | 80 | 1 | 3 | 2 | 3 | 2 | 2 | 2 | 2 |
| 39 | No | Travel_Rarely | 116 | Research & Development | 24 | 1 | Life Sciences | 1 | 2014 | 1 | Male | 52 | 3 | 2 | Research Scientist | 4 | Single | 4108 | 5340 | 7 | Y | No | 13 | 3 | 1 | 80 | 0 | 18 | 2 | 3 | 7 | 7 | 1 | 7 |
| NA | NA | Travel_Rarely | 201 | Research & Development | 10 | 3 | Medical | 1 | 2015 | 2 | Female | 99 | 1 | 3 | Research Director | 3 | Married | 13206 | 3376 | 3 | Y | No | 12 | 3 | 1 | 80 | 1 | 20 | 3 | 3 | 18 | 16 | 1 | 11 |
| 32 | No | Travel_Rarely | 801 | Sales | 1 | 4 | Marketing | 1 | 2016 | 3 | Female | 48 | 3 | 3 | Sales Executive | 4 | Married | 10422 | 24032 | 1 | Y | No | 19 | 3 | 3 | 80 | 2 | 14 | 3 | 3 | 14 | 10 | 5 | 7 |
| 37 | No | Travel_Rarely | 161 | Research & Development | 10 | 3 | Life Sciences | 1 | 2017 | 3 | Female | 42 | 4 | 3 | Research Director | 4 | Married | 13744 | 15471 | 1 | Y | Yes | 25 | 4 | 1 | 80 | 1 | 16 | 2 | 3 | 16 | 11 | 6 | 8 |
| 25 | No | Travel_Rarely | 1382 | Sales | 8 | 2 | Other | 1 | 2018 | 1 | Female | 85 | 3 | 2 | Sales Executive | 3 | Divorced | 4907 | 13684 | 0 | Y | Yes | 22 | 4 | 2 | 80 | 1 | 6 | 3 | 2 | 5 | 3 | 0 | 4 |
| 52 | No | Non-Travel | 585 | Sales | 29 | 4 | Life Sciences | 1 | 2019 | 1 | Male | 40 | 3 | 1 | Sales Representative | 4 | Divorced | 3482 | 19788 | 2 | Y | No | 15 | 3 | 2 | 80 | 2 | 16 | 3 | 2 | 9 | 8 | 0 | 0 |
| 44 | No | Travel_Rarely | 1037 | Research & Development | 1 | 3 | Medical | 1 | 2020 | 2 | Male | 42 | 3 | 1 | Research Scientist | 4 | Single | 2436 | 13422 | 6 | Y | Yes | 12 | 3 | 3 | 80 | 0 | 6 | 2 | 3 | 4 | 3 | 1 | 2 |
| 21 | No | Travel_Rarely | 501 | Sales | 5 | 1 | Medical | 1 | 2021 | 3 | Male | 58 | 3 | 1 | Sales Representative | 1 | Single | 2380 | 25479 | 1 | Y | Yes | 11 | 3 | 4 | 80 | 0 | 2 | 6 | 3 | 2 | 2 | 1 | 2 |
| 39 | No | Non-Travel | 105 | Research & Development | 9 | 3 | Life Sciences | 1 | 2022 | 4 | Male | 87 | 3 | 5 | Manager | 4 | Single | 19431 | 15302 | 2 | Y | No | 13 | 3 | 3 | 80 | 0 | 21 | 3 | 2 | 6 | 0 | 1 | 3 |
| NA | NA | Travel_Frequently | 638 | Sales | 9 | 3 | Marketing | 1 | 2023 | 4 | Male | 33 | 3 | 1 | Sales Representative | 1 | Married | 1790 | 26956 | 1 | Y | No | 19 | 3 | 1 | 80 | 1 | 1 | 3 | 2 | 1 | 0 | 1 | 0 |
| 36 | No | Travel_Rarely | 557 | Sales | 3 | 3 | Medical | 1 | 2024 | 1 | Female | 94 | 2 | 3 | Sales Executive | 4 | Married | 7644 | 12695 | 0 | Y | No | 19 | 3 | 3 | 80 | 2 | 10 | 2 | 3 | 9 | 7 | 3 | 4 |
| 36 | No | Travel_Frequently | 688 | Research & Development | 4 | 2 | Life Sciences | 1 | 2025 | 4 | Female | 97 | 3 | 2 | Manufacturing Director | 2 | Divorced | 5131 | 9192 | 7 | Y | No | 13 | 3 | 2 | 80 | 3 | 18 | 3 | 3 | 4 | 2 | 0 | 2 |
| 56 | No | Non-Travel | 667 | Research & Development | 1 | 4 | Life Sciences | 1 | 2026 | 3 | Male | 57 | 3 | 2 | Healthcare Representative | 3 | Divorced | 6306 | 26236 | 1 | Y | No | 21 | 4 | 1 | 80 | 1 | 13 | 2 | 2 | 13 | 12 | 1 | 9 |
| 29 | NA | Travel_Rarely | 1092 | Research & Development | 1 | 4 | Medical | 1 | 2027 | 1 | Male | 36 | 3 | 1 | Research Scientist | 4 | Married | 4787 | 26124 | 9 | Y | Yes | 14 | 3 | 2 | 80 | 3 | 4 | 3 | 4 | 2 | 2 | 2 | 2 |
| 42 | No | Travel_Rarely | 300 | Research & Development | 2 | 3 | Life Sciences | 1 | 2031 | 1 | Male | 56 | 3 | 5 | Manager | 3 | Married | 18880 | 17312 | 5 | Y | No | 11 | 3 | 1 | 80 | 0 | 24 | 2 | 2 | 22 | 6 | 4 | 14 |
| 56 | Yes | Travel_Rarely | 310 | Research & Development | 7 | 2 | Technical Degree | 1 | 2032 | 4 | Male | 72 | 3 | 1 | Laboratory Technician | 3 | Married | 2339 | 3666 | 8 | Y | No | 11 | 3 | 4 | 80 | 1 | 14 | 4 | 1 | 10 | 9 | 9 | 8 |
| 41 | No | Travel_Rarely | 582 | Research & Development | 28 | 4 | Life Sciences | 1 | 2034 | 1 | Female | 60 | 2 | 4 | Manufacturing Director | 2 | Married | 13570 | 5640 | 0 | Y | No | 23 | 4 | 3 | 80 | 1 | 21 | 3 | 3 | 20 | 7 | 0 | 10 |
| 34 | No | Travel_Rarely | 704 | Sales | 28 | 3 | Marketing | 1 | 2035 | 4 | Female | 95 | 2 | 2 | Sales Executive | 3 | Married | 6712 | 8978 | 1 | Y | No | 21 | 4 | 4 | 80 | 2 | 8 | 2 | 3 | 8 | 7 | 1 | 7 |
| NA | NA | Non-Travel | 301 | Sales | 15 | 4 | Marketing | 1 | 2036 | 4 | Male | 88 | 1 | 2 | Sales Executive | 4 | Divorced | 5406 | 10436 | 1 | Y | No | 24 | 4 | 1 | 80 | 1 | 15 | 4 | 2 | 15 | 12 | 11 | 11 |
| 41 | No | Travel_Rarely | 930 | Sales | 3 | 3 | Life Sciences | 1 | 2037 | 3 | Male | 57 | 2 | 2 | Sales Executive | 2 | Divorced | NA | 12227 | 2 | Y | No | 11 | 3 | 3 | 80 | 1 | 14 | 5 | 3 | 5 | 4 | 0 | 4 |
| 32 | No | Travel_Rarely | 529 | Research & Development | 2 | 3 | Technical Degree | 1 | 2038 | 4 | Male | 78 | 3 | 1 | Research Scientist | 1 | Single | 2439 | 11288 | 1 | Y | No | 14 | 3 | 4 | 80 | 0 | 4 | 4 | 3 | 4 | 2 | 1 | 2 |
| 35 | No | Travel_Rarely | 1146 | Human Resources | 26 | 4 | Life Sciences | 1 | 2040 | 3 | Female | 31 | 3 | 3 | Human Resources | 4 | Single | 8837 | 16642 | 1 | Y | Yes | 16 | 3 | 3 | 80 | 0 | 9 | 2 | 3 | 9 | 0 | 1 | 7 |
| 38 | No | Travel_Rarely | 345 | Sales | 10 | 2 | Life Sciences | 1 | 2041 | 1 | Female | 100 | 3 | 2 | Sales Executive | 4 | Married | 5343 | 5982 | 1 | Y | No | 11 | 3 | 3 | 80 | 1 | 10 | 1 | 3 | 10 | 7 | 1 | 9 |
| 50 | Yes | Travel_Frequently | 878 | Sales | 1 | 4 | Life Sciences | 1 | 2044 | 2 | Male | 94 | 3 | 2 | Sales Executive | 3 | Divorced | 6728 | 14255 | 7 | Y | No | 12 | 3 | 4 | 80 | 2 | 12 | 3 | 3 | 6 | 3 | 0 | 1 |
| 36 | NA | Travel_Rarely | 1120 | Sales | 11 | 4 | Marketing | 1 | 2045 | 2 | Female | 100 | 2 | 2 | Sales Executive | 4 | Married | 6652 | 14369 | 4 | Y | No | 13 | 3 | 1 | 80 | 1 | 8 | 2 | 2 | 6 | 3 | 0 | 0 |
| 45 | No | Travel_Rarely | 374 | Sales | 20 | 3 | Life Sciences | 1 | 2046 | 4 | Female | 50 | 3 | 2 | Sales Executive | 3 | Single | 4850 | 23333 | 8 | Y | No | 15 | 3 | 3 | 80 | 0 | 8 | 3 | 3 | 5 | 3 | 0 | 1 |
| 40 | NA | Travel_Rarely | 1322 | Research & Development | 2 | 4 | Life Sciences | 1 | 2048 | 3 | Male | 52 | 2 | 1 | Research Scientist | 3 | Single | 2809 | 2725 | 2 | Y | No | 14 | 3 | 4 | 80 | 0 | 8 | 2 | 3 | 2 | 2 | 2 | 2 |
| 35 | No | Travel_Frequently | 1199 | Research & Development | 18 | 4 | Life Sciences | 1 | 2049 | 3 | Male | 80 | 3 | 2 | Healthcare Representative | 3 | Married | 5689 | 24594 | 1 | Y | Yes | 14 | 3 | 4 | 80 | 2 | 10 | 2 | 4 | 10 | 2 | 0 | 2 |
| 40 | No | Travel_Rarely | 1194 | Research & Development | 2 | 4 | Medical | 1 | 2051 | 3 | Female | 98 | 3 | 1 | Research Scientist | 3 | Married | 2001 | 12549 | 2 | Y | No | 14 | 3 | 2 | 80 | 3 | 20 | 2 | 3 | 5 | 3 | 0 | 2 |
| 35 | No | Travel_Rarely | 287 | Research & Development | 1 | 4 | Life Sciences | 1 | 2052 | 3 | Female | 62 | 1 | 1 | Research Scientist | 4 | Married | 2977 | 8952 | 1 | Y | No | 12 | 3 | 4 | 80 | 1 | 4 | 5 | 3 | 4 | 3 | 1 | 1 |
| 29 | No | Travel_Rarely | 1378 | Research & Development | 13 | 2 | Other | 1 | 2053 | 4 | Male | 46 | 2 | 2 | Laboratory Technician | 2 | Married | NA | 23679 | 4 | Y | Yes | 13 | 3 | 1 | 80 | 1 | 10 | 2 | 3 | 4 | 3 | 0 | 3 |
| 29 | No | Travel_Rarely | 468 | Research & Development | 28 | 4 | Medical | 1 | 2054 | 4 | Female | 73 | 2 | 1 | Research Scientist | 1 | Single | 3785 | 8489 | 1 | Y | No | 14 | 3 | 2 | 80 | 0 | 5 | 3 | 1 | 5 | 4 | 0 | 4 |
| 50 | Yes | Travel_Rarely | 410 | Sales | 28 | 3 | Marketing | 1 | 2055 | 4 | Male | 39 | 2 | 3 | Sales Executive | 1 | Divorced | 10854 | 16586 | 4 | Y | Yes | 13 | 3 | 2 | 80 | 1 | 20 | 3 | 3 | 3 | 2 | 2 | 0 |
| 39 | No | Travel_Rarely | 722 | Sales | 24 | 1 | Marketing | 1 | 2056 | 2 | Female | 60 | 2 | 4 | Sales Executive | 4 | Married | 12031 | 8828 | 0 | Y | No | 11 | 3 | 1 | 80 | 1 | 21 | 2 | 2 | 20 | 9 | 9 | 6 |
| 31 | No | Non-Travel | 325 | Research & Development | 5 | 3 | Medical | 1 | 2057 | 2 | Male | 74 | 3 | 2 | Manufacturing Director | 1 | Single | 9936 | 3787 | 0 | Y | No | 19 | 3 | 2 | 80 | 0 | 10 | 2 | 3 | 9 | 4 | 1 | 7 |
| 26 | No | Travel_Rarely | 1167 | Sales | 5 | 3 | Other | 1 | 2060 | 4 | Female | 30 | 2 | 1 | Sales Representative | 3 | Single | 2966 | 21378 | 0 | Y | No | 18 | 3 | 4 | 80 | 0 | 5 | 2 | 3 | 4 | 2 | 0 | 0 |
| 36 | No | Travel_Frequently | 884 | Research & Development | 23 | 2 | Medical | 1 | 2061 | 3 | Male | 41 | 4 | 2 | Laboratory Technician | 4 | Married | 2571 | 12290 | 4 | Y | No | 17 | 3 | 3 | 80 | 1 | 17 | 3 | 3 | 5 | 2 | 0 | 3 |
| 39 | No | Travel_Rarely | 613 | Research & Development | 6 | 1 | Medical | 1 | 2062 | 4 | Male | 42 | 2 | 3 | Healthcare Representative | 1 | Married | 9991 | 21457 | 4 | Y | No | 15 | 3 | 1 | 80 | 1 | 9 | 5 | 3 | 7 | 7 | 1 | 7 |
| 27 | No | Travel_Rarely | 155 | Research & Development | 4 | 3 | Life Sciences | 1 | 2064 | 2 | Male | 87 | 4 | 2 | Manufacturing Director | 2 | Married | 6142 | 5174 | 1 | Y | Yes | 20 | 4 | 2 | 80 | 1 | 6 | 0 | 3 | 6 | 2 | 0 | 3 |
| 49 | No | Travel_Frequently | 1023 | Sales | 2 | 3 | Medical | 1 | 2065 | 4 | Male | 63 | 2 | 2 | Sales Executive | 2 | Married | 5390 | 13243 | 2 | Y | No | 14 | 3 | 4 | 80 | 0 | 17 | 3 | 2 | 9 | 6 | 0 | 8 |
| 34 | No | Travel_Rarely | 628 | Research & Development | 8 | 3 | Medical | 1 | 2068 | 2 | Male | 82 | 4 | 2 | Laboratory Technician | 3 | Married | 4404 | 10228 | 2 | Y | No | 12 | 3 | 1 | 80 | 0 | 6 | 3 | 4 | 4 | 3 | 1 | 2 |
Zbiór danych HR zawiera informacje dotyczące 1,470
obecnych i byłych pracowników zebranych w 35 kolumnach, w tym związane z
ich satysfakcją z pracy, równowagą między życiem zawodowym a prywatnym,
stażem pracy, doświadczeniem, wynagrodzeniem oraz innych cech
demograficznych.
| Name | HR |
| Number of rows | 1470 |
| Number of columns | 35 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| numeric | 26 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Attrition | 150 | 0.9 | 2 | 3 | 0 | 2 | 0 |
| BusinessTravel | 0 | 1.0 | 10 | 17 | 0 | 3 | 0 |
| Department | 0 | 1.0 | 5 | 22 | 0 | 3 | 0 |
| EducationField | 0 | 1.0 | 5 | 16 | 0 | 6 | 0 |
| Gender | 0 | 1.0 | 4 | 6 | 0 | 2 | 0 |
| JobRole | 0 | 1.0 | 7 | 25 | 0 | 9 | 0 |
| MaritalStatus | 0 | 1.0 | 6 | 8 | 0 | 3 | 0 |
| Over18 | 0 | 1.0 | 1 | 1 | 0 | 1 | 0 |
| OverTime | 0 | 1.0 | 2 | 3 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Age | 100 | 0.93 | 36.98 | 9.14 | 18 | 30.00 | 36.0 | 43.00 | 60 | ▂▇▇▃▂ |
| DailyRate | 0 | 1.00 | 802.49 | 403.51 | 102 | 465.00 | 802.0 | 1157.00 | 1499 | ▇▇▇▇▇ |
| DistanceFromHome | 0 | 1.00 | 9.19 | 8.11 | 1 | 2.00 | 7.0 | 14.00 | 29 | ▇▅▂▂▂ |
| Education | 0 | 1.00 | 2.91 | 1.02 | 1 | 2.00 | 3.0 | 4.00 | 5 | ▂▃▇▆▁ |
| EmployeeCount | 0 | 1.00 | 1.00 | 0.00 | 1 | 1.00 | 1.0 | 1.00 | 1 | ▁▁▇▁▁ |
| EmployeeNumber | 0 | 1.00 | 1024.87 | 602.02 | 1 | 491.25 | 1020.5 | 1555.75 | 2068 | ▇▇▇▇▇ |
| EnvironmentSatisfaction | 0 | 1.00 | 2.72 | 1.09 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| HourlyRate | 0 | 1.00 | 65.89 | 20.33 | 30 | 48.00 | 66.0 | 83.75 | 100 | ▇▇▇▇▇ |
| JobInvolvement | 0 | 1.00 | 2.73 | 0.71 | 1 | 2.00 | 3.0 | 3.00 | 4 | ▁▃▁▇▁ |
| JobLevel | 0 | 1.00 | 2.06 | 1.11 | 1 | 1.00 | 2.0 | 3.00 | 5 | ▇▇▃▂▁ |
| JobSatisfaction | 0 | 1.00 | 2.73 | 1.10 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| MonthlyIncome | 150 | 0.90 | 6523.42 | 4721.48 | 1051 | 2922.25 | 4933.0 | 8400.00 | 19973 | ▇▅▂▁▂ |
| MonthlyRate | 0 | 1.00 | 14313.10 | 7117.79 | 2094 | 8047.00 | 14235.5 | 20461.50 | 26999 | ▇▇▇▇▇ |
| NumCompaniesWorked | 0 | 1.00 | 2.69 | 2.50 | 0 | 1.00 | 2.0 | 4.00 | 9 | ▇▃▂▂▁ |
| PercentSalaryHike | 0 | 1.00 | 15.21 | 3.66 | 11 | 12.00 | 14.0 | 18.00 | 25 | ▇▅▃▂▁ |
| PerformanceRating | 0 | 1.00 | 3.15 | 0.36 | 3 | 3.00 | 3.0 | 3.00 | 4 | ▇▁▁▁▂ |
| RelationshipSatisfaction | 0 | 1.00 | 2.71 | 1.08 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| StandardHours | 0 | 1.00 | 80.00 | 0.00 | 80 | 80.00 | 80.0 | 80.00 | 80 | ▁▁▇▁▁ |
| StockOptionLevel | 0 | 1.00 | 0.79 | 0.85 | 0 | 0.00 | 1.0 | 1.00 | 3 | ▇▇▁▂▁ |
| TotalWorkingYears | 0 | 1.00 | 11.28 | 7.78 | 0 | 6.00 | 10.0 | 15.00 | 40 | ▇▇▂▁▁ |
| TrainingTimesLastYear | 0 | 1.00 | 2.80 | 1.29 | 0 | 2.00 | 3.0 | 3.00 | 6 | ▂▇▇▂▃ |
| WorkLifeBalance | 0 | 1.00 | 2.76 | 0.71 | 1 | 2.00 | 3.0 | 3.00 | 4 | ▁▃▁▇▂ |
| YearsAtCompany | 0 | 1.00 | 7.01 | 6.13 | 0 | 3.00 | 5.0 | 9.00 | 40 | ▇▂▁▁▁ |
| YearsInCurrentRole | 0 | 1.00 | 4.23 | 3.62 | 0 | 2.00 | 3.0 | 7.00 | 18 | ▇▃▂▁▁ |
| YearsSinceLastPromotion | 0 | 1.00 | 2.19 | 3.22 | 0 | 0.00 | 1.0 | 3.00 | 15 | ▇▁▁▁▁ |
| YearsWithCurrManager | 0 | 1.00 | 4.12 | 3.57 | 0 | 2.00 | 3.0 | 7.00 | 17 | ▇▂▅▁▁ |
Analizując strukturę pliku z danymi, możemy wyciągnąć kilka istotnych wniosków dotyczących zarówno samego zbioru danych, jak i poszczególnych zmiennych. Każdego pracownika reprezentuje zbiór 35 zmiennych, z których 26 to zmienne numeryczne, a 9 to zmienne kategoryczne (tekstowe).
W analizowanych danych zauważamy kilka istotnych problemów, które warto uwzględnić w dalszym etapie analizy. Zauważyć możemy występowanie braków danych trzech zmiennych tj. Age, Attrition oraz MonthlyIncome. Wskazuje to na konieczność rozważenia metod uzupełniania tych braków, aby uniknąć ich wpływu na wyniki analizy.
Dodatkowo, w przypadku zmiennej NumCompaniesWorked wartości równe 0 mogą budzić wątpliwości, ponieważ sugerują, że pracownik nie posiada doświadczenia zawodowego w innych przedsiębiorstwach, co, zwłaszcza w przypadku starszych pracowników może być nietypowe. Tego rodzaju wartości należy dokładniej przeanalizować, aby zweryfikować, czy są one wynikiem błędu w rejestracji danych, czy też stanowią rzeczywisty przypadek (np. brak wcześniejszego zatrudnienia).
2. Wstępna analiza danych
2.1. Weryfikacja poprawności danych
Przed przystąpieniem do dalszej analizy danych, niezwykle istotne jest upewnienie się, że dane spełniają określone założenia w celu zapewnienia ich poprawności i spójności. Poniżej przedstawiono kluczowe założenia, które należy zweryfikować przed rozpoczęciem właściwej analizy danych:
- zmienne numeryczne w zestawie danych powinny przyjmować wyłącznie wartości nieujemne;
- pracownicy spełniają wymagania związane z pełnoletnością, wiekiem emerytalnym oraz z dotychczasowym stażem pracy;
- zmienna dotycząca odejść pracowników powinna ona przyjmować jedynie dwie kategorie: „Yes” dla osób, które opuściły firmę, oraz „No” dla pracowników pozostających w firmie;
- w przypadku pracowników, dla których ta firma nie była pierwszym pracodawcą, konieczne jest odpowiednie odnotowanie tej informacji;
- szkolenia pracowników odbywają się nie częściej, niż co dwa miesiące;
- czas przepracowany w firmie może być krótszy niż czas od ostatniego awansu, lata pracy z tym samym menedżerem lub lata na obecnym stanowisku;
- prawidłowo uzależniona wartość podwyżki od oceny pracy pracownika;
- należycie przypisano sprawowane stanowiska do działów przedsiębiorstwa.
walidacja_danych <- function(HR) {
HR %>% mutate(
AgeRule = if_else(
Age >= 18 & Age <= 67 & Age >= (18 + TotalWorkingYears) & is.numeric(Age),
TRUE,
FALSE,
missing = NA
),
AttritionRule = if_else(
Attrition %in% c("Yes", "No"),
TRUE,
NA,
missing = NA
),
DistanceFromHomeRule = if_else(
DistanceFromHome >= 0 & is.numeric(DistanceFromHome),
TRUE,
FALSE,
missing = NA
),
DailyRateRule = if_else(
DailyRate > 0 & is.numeric(DailyRate),
TRUE,
FALSE,
missing = NA
),
HourlyRateRule = if_else(
is.numeric(HourlyRate) & HourlyRate > 0,
TRUE,
FALSE,
missing = NA
),
MonthlyIncomeRule = if_else(
is.numeric(MonthlyIncome) & MonthlyIncome> 0,
TRUE,
FALSE,
missing = NA
),
MonthlyRateRule = if_else(
is.numeric(MonthlyRate) & MonthlyRate > 0,
TRUE,
FALSE,
missing = NA
),
NumCompaniesWorkedRule = if_else(
is.numeric(NumCompaniesWorked) &
(
NumCompaniesWorked > 0 | (NumCompaniesWorked == 0 & YearsAtCompany == TotalWorkingYears)
),
TRUE,
FALSE,
missing = NA
),
TotalWorkingYearsRule = if_else(
is.numeric(TotalWorkingYears) & TotalWorkingYears >= 0,
TRUE,
FALSE,
missing = NA
),
TrainingTimesLastYearRule = if_else(
is.numeric(TrainingTimesLastYear) & TrainingTimesLastYear >= 0 & TrainingTimesLastYear <= 6,
TRUE,
FALSE,
missing = NA
),
YearsAtCompanyRule = if_else(
is.numeric(YearsAtCompany) &
YearsAtCompany >= 0 & YearsAtCompany <= 49 & (
(YearsAtCompany >= YearsInCurrentRole) |
(YearsAtCompany >= YearsSinceLastPromotion) |
(YearsAtCompany >= YearsWithCurrManager)
), TRUE,
FALSE,
missing = NA
),
YearsInCurrentRoleRule = if_else(
is.numeric(YearsInCurrentRole) & YearsInCurrentRole >= 0 & YearsInCurrentRole < 49,
TRUE,
FALSE,
missing = NA
),
YearsSinceLastPromotionRule = if_else(
is.numeric(YearsSinceLastPromotion) & YearsSinceLastPromotion >= 0,
TRUE,
FALSE,
missing = NA
),
YearsWithCurrManagerRule = if_else(
is.numeric(YearsWithCurrManager) & YearsWithCurrManager >= 0,
TRUE,
FALSE,
missing = NA
),
PercentSalaryHikeRule = if_else(
(PerformanceRating == 1 & PercentSalaryHike == 0) |
(PerformanceRating == 2 & PercentSalaryHike >= 1 & PercentSalaryHike <= 10) |
(PerformanceRating == 3 & PercentSalaryHike >= 11 & PercentSalaryHike <= 19) |
(PerformanceRating == 4 & PercentSalaryHike >= 20),
TRUE,
FALSE,
missing = NA
),
JobRoleRule = if_else(
(Department == "Sales" & JobRole %in% c("Sales Executive", "Sales Representative", "Manager")) |
(Department == "Research & Development" & JobRole %in% c("Research Scientist", "Laboratory Technician",
"Research Director", "Manufacturing Director",
"Healthcare Representative", "Manager")) |
(Department == "Human Resources" & JobRole %in% c("Human Resources", "Manager")),
TRUE,
FALSE,
missing = NA
)
)
}ggplot(wykres_walidacja_fin, aes(x = Rule, y = Count, fill = Status)) +
geom_bar(stat = "identity", position = "stack") +
scale_fill_manual(values = kolory_walidacja, labels = wykres_walidacja_legenda) +
theme_minimal() +
coord_flip() +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
legend.position = "bottom",
legend.justification = "right",
legend.title = element_blank(),
plot.title = element_blank()
)
| Rule | N Passed | N Failed | N NA | Fail % | Pass % | NA % |
|---|---|---|---|---|---|---|
| Age | 1370 | 0 | 100 | 0.0% | 93.2% | 6.8% |
| Attrition | 1320 | 0 | 150 | 0.0% | 89.8% | 10.2% |
| DailyRate | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| DistanceFromHome | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| HourlyRate | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| JobRole | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| MonthlyIncome | 1320 | 0 | 150 | 0.0% | 89.8% | 10.2% |
| MonthlyRate | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| NumCompaniesWorked | 1273 | 197 | 0 | 13.4% | 86.6% | 0.0% |
| PercentSalaryHike | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| TotalWorkingYears | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| TrainingTimesLastYear | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| YearsAtCompany | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| YearsInCurrentRole | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| YearsSinceLastPromotion | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
| YearsWithCurrManager | 1470 | 0 | 0 | 0.0% | 100.0% | 0.0% |
Zauważono, że w przypadku zmiennej NumCompaniesWorked zarejestrowano 197 pracowników, których dotychczasowy staż pracy przekraczał czas zatrudnienia w analizowanej organizacji, a jednocześnie zmienna ta przyjmowała wartośc 0 . Tego rodzaju rozbieżność może wskazywać na błędy w rejestracji danych lub na niezgodność między doświadczeniem zawodowym a zarejestrowaną historią zatrudnienia w firmie. Aby zapewnić poprawność dalszej analizy zależności pomiędzy odejściem pracowników a innymi zmiennymi, w przypadku tych rozbieżności zostaną wprowadzone braki, co pozwoli uniknąć potencjalnego zniekształcenia wyników analizy.
2.2. Struktura danych po walidacji
## Rows: 1,470
## Columns: 35
## $ Age <dbl> NA, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 2…
## $ Attrition <chr> "Yes", "No", NA, "No", "No", "No", "No", "No"…
## $ BusinessTravel <chr> "Travel_Rarely", "Travel_Frequently", "Travel…
## $ DailyRate <dbl> 1102, 279, 1373, 1392, 591, 1005, 1324, 1358,…
## $ Department <chr> "Sales", "Research & Development", "Research …
## $ DistanceFromHome <dbl> 1, 8, 2, 3, 2, 2, 3, 24, 23, 27, 16, 15, 26, …
## $ Education <dbl> 2, 1, 2, 4, 1, 2, 3, 1, 3, 3, 3, 2, 1, 2, 3, …
## $ EducationField <chr> "Life Sciences", "Life Sciences", "Other", "L…
## $ EmployeeCount <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ EmployeeNumber <dbl> 1, 2, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 16,…
## $ EnvironmentSatisfaction <dbl> 2, 3, 4, 4, 1, 4, 3, 4, 4, 3, 1, 4, 1, 2, 3, …
## $ Gender <chr> "Female", "Male", "Male", "Female", "Male", "…
## $ HourlyRate <dbl> 94, 61, 92, 56, 40, 79, 81, 67, 44, 94, 84, 4…
## $ JobInvolvement <dbl> 3, 2, 2, 3, 3, 3, 4, 3, 2, 3, 4, 2, 3, 3, 2, …
## $ JobLevel <dbl> 2, 2, 1, 1, 1, 1, 1, 1, 3, 2, 1, 2, 1, 1, 1, …
## $ JobRole <chr> "Sales Executive", "Research Scientist", "Lab…
## $ JobSatisfaction <dbl> 4, 2, 3, 3, 2, 4, 1, 3, 3, 3, 2, 3, 3, 4, 3, …
## $ MaritalStatus <chr> "Single", "Married", "Single", "Married", "Ma…
## $ MonthlyIncome <dbl> 5993, NA, 2090, NA, 3468, 3068, 2670, 2693, 9…
## $ MonthlyRate <dbl> 19479, 24907, 2396, 23159, 16632, 11864, 9964…
## $ NumCompaniesWorked <dbl> 8, 1, 6, 1, 9, NA, 4, 1, NA, 6, NA, NA, 1, NA…
## $ Over18 <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", …
## $ OverTime <chr> "Yes", "No", "Yes", "Yes", "No", "No", "Yes",…
## $ PercentSalaryHike <dbl> 11, 23, 15, 11, 12, 13, 20, 22, 21, 13, 13, 1…
## $ PerformanceRating <dbl> 3, 4, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, 3, …
## $ RelationshipSatisfaction <dbl> 1, 4, 2, 3, 4, 3, 1, 2, 2, 2, 3, 4, 4, 3, 2, …
## $ StandardHours <dbl> 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 8…
## $ StockOptionLevel <dbl> 0, 1, 0, 0, 1, 0, 3, 1, 0, 2, 1, 0, 1, 1, 0, …
## $ TotalWorkingYears <dbl> 8, 10, 7, 8, 6, 8, 12, 1, 10, 17, 6, 10, 5, 3…
## $ TrainingTimesLastYear <dbl> 0, 3, 3, 3, 3, 2, 3, 2, 2, 3, 5, 3, 1, 2, 4, …
## $ WorkLifeBalance <dbl> 1, 3, 3, 3, 3, 2, 2, 3, 3, 2, 3, 3, 2, 3, 3, …
## $ YearsAtCompany <dbl> 6, 10, 0, 8, 2, 7, 1, 1, 9, 7, 5, 9, 5, 2, 4,…
## $ YearsInCurrentRole <dbl> 4, 7, 0, 7, 2, 7, 0, 0, 7, 7, 4, 5, 2, 2, 2, …
## $ YearsSinceLastPromotion <dbl> 0, 1, 0, 3, 2, 3, 0, 0, 1, 7, 0, 0, 4, 1, 0, …
## $ YearsWithCurrManager <dbl> 5, 7, 0, 0, 2, 6, 0, 0, 8, 7, 3, 8, 3, 2, 3, …Graficzną prezentację danych prezentuje poniższy wykres. Zauważyć możemy, iż zmienne kategoryczne i numeryczne występują naprzemiennie, a braki dominują w zmiennych numerycznych.
## # A tibble: 35 × 3
## variable n_miss pct_miss
## <chr> <int> <num>
## 1 NumCompaniesWorked 197 13.4
## 2 Attrition 150 10.2
## 3 MonthlyIncome 150 10.2
## 4 Age 100 6.80
## 5 BusinessTravel 0 0
## 6 DailyRate 0 0
## 7 Department 0 0
## 8 DistanceFromHome 0 0
## 9 Education 0 0
## 10 EducationField 0 0
## # ℹ 25 more rows## # A tibble: 4 × 3
## n_miss_in_case n_cases pct_cases
## <int> <int> <dbl>
## 1 0 953 64.8
## 2 1 441 30
## 3 2 72 4.90
## 4 3 4 0.272W zbiorze danych brakuje 597 wartości zmiennych, co stanowi 1.16035% wszystkich wartości zmiennych. 441 rzedów cechuje się brakiem jednej ze zmiennych, 72 rzędy dwoma brakami oraz w 4 rzędach brakuje wartości dla 3 zmiennych.
unique_values_list <- lapply(HR[, sapply(HR, is.character)], unique)
unique_values_list_padded <- lapply(unique_values_list, function(x) {
length(x) <- max(sapply(unique_values_list, length))
return(x)
})
unique_values_df <- as.data.frame(unique_values_list_padded)
colnames(unique_values_df) <- names(unique_values_list)
unique_values_df %>%
kable("html", caption = "Unikalne wartości tekstowe w kolumnach danych HR") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>%
scroll_box(width = "100%", height = "300px")| Attrition | BusinessTravel | Department | EducationField | Gender | JobRole | MaritalStatus | Over18 | OverTime |
|---|---|---|---|---|---|---|---|---|
| Yes | Travel_Rarely | Sales | Life Sciences | Female | Sales Executive | Single | Y | Yes |
| No | Travel_Frequently | Research & Development | Other | Male | Research Scientist | Married | NA | No |
| NA | Non-Travel | Human Resources | Medical | NA | Laboratory Technician | Divorced | NA | NA |
| NA | NA | NA | Marketing | NA | Manufacturing Director | NA | NA | NA |
| NA | NA | NA | Technical Degree | NA | Healthcare Representative | NA | NA | NA |
| NA | NA | NA | Human Resources | NA | Manager | NA | NA | NA |
| NA | NA | NA | NA | NA | Sales Representative | NA | NA | NA |
| NA | NA | NA | NA | NA | Research Director | NA | NA | NA |
| NA | NA | NA | NA | NA | Human Resources | NA | NA | NA |
W powyższej ramce danych zawarto unikalne wartości dla każdej kolumny tekstowej, co pozwali na analizę możliwych odpowiedzi w zmiennych kategorycznych. Stwierdzić należy, iż nie wykryto niekonsekwencji w danych, takich jak różne formaty zapisu, literówki czy nieoczekiwane wartości, za wyjątkiem zmiennej Over18, która przyjmuje tylko jedną wartość.
limited_numeric_values <- function(df, threshold = 10) {
numeric_cols <- names(df)[sapply(df, is.numeric) & sapply(df, function(x) n_distinct(x) < threshold)]
if (length(numeric_cols) == 0) return(data.frame())
unique_values_list <- lapply(df[numeric_cols], function(x) sort(unique(x)))
max_length <- max(sapply(unique_values_list, length))
unique_values_df <- as.data.frame(lapply(unique_values_list, `length<-`, max_length))
unique_values_df <- unique_values_df[rowSums(!is.na(unique_values_df)) > 0, ]
return(unique_values_df)
}
limited_numeric_values(HR) %>%
kable("html", caption = "Unikalne wartości dla kolumn numerycznych (poniżej progu)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>%
scroll_box(width = "100%", height = "300px")| Education | EmployeeCount | EnvironmentSatisfaction | JobInvolvement | JobLevel | JobSatisfaction | PerformanceRating | RelationshipSatisfaction | StandardHours | StockOptionLevel | TrainingTimesLastYear | WorkLifeBalance |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 80 | 0 | 0 | 1 |
| 2 | NA | 2 | 2 | 2 | 2 | 4 | 2 | NA | 1 | 1 | 2 |
| 3 | NA | 3 | 3 | 3 | 3 | NA | 3 | NA | 2 | 2 | 3 |
| 4 | NA | 4 | 4 | 4 | 4 | NA | 4 | NA | 3 | 3 | 4 |
| 5 | NA | NA | NA | 5 | NA | NA | NA | NA | NA | 4 | NA |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 5 | NA |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 6 | NA |
Powyższa ramka prezentuje wartości zmiennych numerycznych posiadających mniej niż 10 unikatowych wartości. Zmienne te można uznać za zmienne kategoryczne, za wyjątkiem zmiennych EmployeeCount oraz StandardHours przyjmujących tylko jedną wartość, oraz TrainingTimesLastYear, reprezentującej liczbę odbytych przez pracownika szkoleń w roku poprzednim, która powinna być traktowana jako zmienna numeryczna.
unique_columns <- function(df) {
col_names <- names(df)
unique_cols <- col_names[sapply(df, function(x) {
is.vector(x) && !any(duplicated(x)) && (!is.numeric(x) || all(x == round(x)))
})]
return(unique_cols)
}Zbiór danych zawiera ponadto jedną zmienną EmployeeNumber, która to posiada wyłącznie unikatowe wartości. Stanowią one unikalne identyfikatory pracownika, które nie wnoszą istotnych informacji dla analizy.
2.3. Wizualna identyfikacja braków danych
Analiza graficzna braków danych pozwala na szybkie zidentyfikowanie obszarów, w których występują niekompletne informacje. Dzięki wizualizacji możliwe jest określenie rozkładu brakujących wartości w poszczególnych zmiennych oraz dostrzeżenie potencjalnych zależności między nimi. Pozwala to lepiej zrozumieć strukturę danych i ocenić charakter braków, co warunkować będzie wybór odpowiednich metod imputacji.
2.3.1 Analiza grupowań braków danych
Powyższy wykres przedstawia uporządkowaną strukturę, w której obserwacje o podobnych wzorcach braków są ze sobą grupowane, co umożliwia lepszą identyfikację występujących zależności, a uporządkowanie kolumn według częstości braków pozwala na łatwiejsze zlokalizowanie zmiennych z najwyższym odsetkiem brakujących wartości. Na jego podstawie zauważyć można, że większość braków zmiennych jest niezależna od braków innych zmiennych, tylko nieznaczna część braków współwystępuje z brakami innych zmiennych.
Powyższe obserwacje potwierdza wykres typu UpSet, na podstawie którego możemy zauważyć interesujące zależności między zmiennymi. Wszczególności braki danych w różnych zmiennych występują w następujących kombinacjach:
- Age i Attrition – 12 przypadków,
- Age i NumCompaniesWorked - 11 przypadków,
- Age i MonthlyIncome – 7 przypadków,
- Age, MonthlyIncome i NumCompaniesWorked – 2 przypadki,
- Age, Attrition i MonthlyIncome – 2 przypadki,
- Attrition i NumCompaniesWorked – 16 przypadków,
- Attrition i MonthlyIncome – 11 przypadków,
- Attrition, MonthlyIncome i NumCompaniesWorked – 2 przypadki,
- MothlyIncome i NumCompaniesWorked - 15 przypadków.
Zauważyć można, iż powyższe wykresy potwierdzają grupowanie się braków danych. Stwierdzić można, iż braki przyjmują wzorzec wielowymiarowy (braki w więcej niż jednej zmiennej), niemonotonny (braki jedej zmiennej nie oznaczają braków pozostałych zmiennych) oraz połączony, gdyż braki nie obejmują całej zmiennej, a tym samym pozwalają na określenie zależności pomiędzy zmiennymi, w tym korelacji.
Kombinacje braków wartości zmiennych, sugerują iż mogą być one całkowicie losowe (Missing Completely At Random) lub są zależne od innych zmiennych w zbiorze (Missing At Random), co wskazuje na konieczność zastosowania bardziej zaawansowanych metod imputacji.
2.3.2. Analiza współwystepowania braków
Wykres rozrzutu zmiennych ciągłych, takich jak Age i MonthlyIncome, dobrze obrazuje zależności między obserwacjami, gdyż każda wartość jest unikalnie rozmieszczona na osi, co umożliwia łatwe wykrycie zależności zarówno pomiędzy brakami danych, jak i samymi danymi.
Natomiast w przypadku zmiennych dyskretnych, takich jak Attrition (zmienna binarna) i NumCompaniesWorked (przyjmująca ograniczoną liczbę wartości), wykres rozrzutu charakteryzuje się nakładania się punktów, natomiast inne typy wykresów nie radzą sobie z prezentacją zależności pomiędzy brakami w wartościach poszczególnych zmiennych. Zastosowanie wykresów dla tychże zmiennych nie przyniesie tym samym dodatkowych informacji, można jednakże zastosować Attrition do grupowania zmiennych ciągłych, przy czym zmienna NumCompaniesWorked posiada zbyt wiele wartości, co uniemożliwia jej efektywne zastosowanie w tym celu.
ami_sc_viz1 <- ggplot(data = HR, aes(x = Age, y = MonthlyIncome)) +
geom_point() +
geom_miss_point() +
geom_smooth(method = "lm", se = FALSE, na.rm = TRUE, color = "black") +
scale_color_manual(
name = "Brakujące wartości",
values = c("darkorange", "cyan4"),
labels = c("występują", "nie występują")
) +
labs(
x = "Age",
y = "MonthlyIncome"
) +
theme_minimal() +
theme(legend.position = "bottom")
ami_sc_viz2 <- ggplot(data = HR, aes(x = Age, y = MonthlyIncome, color = Attrition, group = Attrition)) +
geom_point() +
geom_miss_point() +
geom_smooth(method = "lm", se = FALSE, na.rm = TRUE) +
scale_color_paletteer_d("khroma::bright", name = "Attrition", direction = 1, na.value = "black") +
labs(x = "Age", y = "MonthlyIncome") +
theme_minimal() +
theme(legend.position = "bottom")
ami_sc_vizcomb <- wrap_plots(ami_sc_viz1, ami_sc_viz2, nrow = 1)
ami_sc_vizcomb
Możemy zauważyć, że zmienne MonthlyIncome oraz Age wykazują dodatnią korelację – wraz ze wzrostem wieku miesięczney dochód rośnie. Linia trendu potwierdza tę zależność, sugerując, że starsi pracownicy generalnie osiągają wyższe dochody. Braki danych co do wieku pracownika (oznaczone na osi pionowej) dotyczą głównie pracowników o niższym i średnim dochodzie, natomiast braki danych, co do miesięcznego dochodu (oznaczone na osi poziomej) cechują się mniej więcej równym rozkładem.
Dodatkowo zauważyć należy, iż braki w zmiennych Age i MonthlyIncome nie wykazują szczególnie wyraźnego uzależnienia od tego, czy pracownik odszedł z przedsiębiorstwa, natomiast brak danych dotyczących odejść dotyczy głównie pracowników relatywnie młodszych i o niższym miesięcznym dochodzie.
Warto przy tym zaznaczyć, że metodę regresji liniowej, zastosowano wyłącznie do kompletnych przypadków, co minimalizuje potencjalne zniekształcenia, jakie mogłyby wyniknąć z uśredniania wartości przy obecności braków.
2.4. Test Little’a MCAR (Missing Completely at Random)
W celu sprawdzenia, czy brakujące dane są brakujące całkowicie losowo (MCAR), przeprowadzono test statystyczny, który ocenia tę hipotezę. Jest to istotne, ponieważ jeśli dane są MCAR, możemy uznać, że brakujące wartości nie wprowadzają uprzedzeń do analiz i bezpiecznie zastosować metody imputacji braków.
W kontekście wcześniejszych obserwacji wykresów, gdzie mogły pojawić się pewne wstępne sygnały co do charakteru braków danych, przeprowadzenie testu Little’a MCAR umożliwia statystyczną weryfikację tych obserwacji. Test ten, zaproponowany przez R. Little’a (1988), służy do sprawdzenia, czy brakujące dane w zbiorze są całkowicie losowe. W praktyce test ten ocenia, czy wzorce braków danych nie wykazują żadnej systematycznej zależności od wartości obserwowanych (ani nieobserwowanych) zmiennych.
Hipoteza testu:
- Hipoteza zerowa (H₀): Dane są brakujące całkowicie losowo (MCAR). Oznacza to, że brakujące wartości nie mają żadnej zależności ani z obserwowanymi, ani z nieobserwowanymi zmiennymi.
- Hipoteza alternatywna (H₁): Dane nie są brakujące całkowicie losowo, czyli brakujące wartości są powiązane z obserwowanymi lub nieobserwowanymi zmiennymi w zbiorze danych.
HR_mcar_test <- HR %>%
mutate(across(where(is.character), as.factor)) %>%
mutate(across(where(is.factor), ~ as.numeric(.))) %>%
remove_constant()
mcar_test(HR_mcar_test)## # A tibble: 1 × 4
## statistic df p.value missing.patterns
## <dbl> <dbl> <dbl> <int>
## 1 426. 391 0.109 14Interpretacja:
- Wartość p = 0,109 (większa od typowego poziomu istotności 0,05) wskazuje, że nie ma wystarczających dowodów, aby odrzucić hipotezę zerową (H₀). Oznacza to, że brakujące dane można uznać za brakujące całkowicie losowo (MCAR).
- Liczba wzorców brakujących danych wynosząca 14 wskazuje, że w zbiorze występuje 14 różnych kombinacji zmiennych z brakującymi wartościami, co potwierdza wcześniejsze obserwacje.
Wykorzystana metoda w pakiecie naniar stanowi
implementację testu Little’a MCAR, o której więcej można przeczytać w
oryginalnym artykule:
Little, R. J. A. (1988). “A Test of Missing Completely at Random for Multivariate Data with Missing Values.” Journal of the American Statistical Association, 83(404), 1198–1202.
2.5. Zidentyfikowane problemy w bazie danych
- Łączna liczba brakujących danych w zbiorze wynosi r n_miss(HR). Konieczne jest tym samym zastosowanie metod uzupełnienia braków danych uwzględniających złożony charakter braków danych.
- Zmienne tekstowe Department, EducationField, Gender, JobRole oraz MaritalStatus powinny być zakodowane jako zmienne kategorialne (factor), podczas gdy zmienne tekstowe Attrition, BusinessTravel, OverTime oraz numeryczne Education, EnvironmentSatisfaction, JobInvolvement, JobLevel, JobSatisfaction, PerformanceRating, RelationshipSatisfaction, StockOptionLevel oraz WorkLifeBalance, powinny być uporządkowanymi zmiennymi kategorialnymi (ordered factor).
- Zmienne EmployeeCount, EmployeeNumber, StandardHours oraz Over18 nie dostarczają nowych informacji analitycznych i zostaną usunięte ze zbioru danych.
3. Czyszczenie danych
3.1. Usuwanie zbędnych kolumn
3.2. Przekodowanie zmiennych
Poniższy kod przekształca wskazane w rozdziale 2.5. zmienne na zmienne kategorialne (factor) oraz uporządkowene kategorialne (ordered factor) z nadaniem odpowiednich etykiet (labels). Jest to standardowa praktyka w w przygotowaniu danych do analizy statystycznej. Umożliwia to stosowanie różnych metod statystycznych i modeli, oraz pozwala na łatwą konwersję na zmienne numeryczne, jeżeli jest to wymagane.
HR_czyste <- HR_czyste %>%
mutate(
# Zmienne kategorialne nieuporządkowane (bazowe tekstowe)
Department = factor(case_when(
Department == "Human Resources" ~ 1,
Department == "Research & Development" ~ 2,
Department == "Sales" ~ 3
), levels = 1:3, labels = c("Kadry", "Badania i Rozwój", "Sprzedaż"), ordered = FALSE),
EducationField = factor(case_when(
EducationField == "Human Resources" ~ 1,
EducationField == "Life Sciences" ~ 2,
EducationField == "Marketing" ~ 3,
EducationField == "Medical" ~ 4,
EducationField == "Technical Degree" ~ 5,
EducationField == "Other" ~ 6
), levels = 1:6, labels = c("Zarządzanie Zasobami Ludzkimi", "Nauki Przyrodnicze", "Marketing", "Medyczne", "Techniczne", "Inne"), ordered = FALSE),
Gender = factor(case_when(
Gender == "Female" ~ 0,
Gender == "Male" ~ 1
), levels = 0:1, labels = c("Kobieta", "Mężczyzna"), ordered = FALSE),
JobRole = factor(case_when(
JobRole == "Healthcare Representative" ~ 1,
JobRole == "Human Resources" ~ 2,
JobRole == "Laboratory Technician" ~ 3,
JobRole == "Manager" ~ 4,
JobRole == "Manufacturing Director" ~ 5,
JobRole == "Research Director" ~ 6,
JobRole == "Research Scientist" ~ 7,
JobRole == "Sales Executive" ~ 8,
JobRole == "Sales Representative" ~ 9
), levels = 1:9, labels = c("Przedstawiciel Medyczny", "Pracownik Działu Kadr", "Technik Laboratoryjny", "Menedżer", "Dyrektor Produkcji", "Dyrektor Badań", "Pracownik Działu Badań", "Dyrektor Sprzedaży", "Przedstawiciel Handlowy"), ordered = FALSE),
MaritalStatus = factor(case_when(
MaritalStatus == "Single" ~ 1,
MaritalStatus == "Married" ~ 2,
MaritalStatus == "Divorced" ~ 3
), levels = 1:3, labels = c("Panna/Kawaler", "Żonata/y", "Rozwiedziona/y"), ordered = FALSE),
# Zmienne kategorialne uporządkowane (bazowe tekstowe)
Attrition = factor(case_when(
Attrition == "No" ~ 0,
Attrition == "Yes" ~ 1
), levels = 0:1, labels = c("Nie", "Tak"), ordered = TRUE),
BusinessTravel = factor(case_when(
BusinessTravel == "Non-Travel" ~ 0,
BusinessTravel == "Travel_Rarely" ~ 1,
BusinessTravel == "Travel_Frequently" ~ 2
), levels = 0:2, labels = c("Brak", "Rzadko", "Często"), ordered = TRUE),
OverTime = factor(case_when(
OverTime == "No" ~ 0,
OverTime == "Yes" ~ 1
), levels = 0:1, labels = c("Nie", "Tak"), ordered = TRUE),
# Zmienne kategorialne uporządkowane (bazowe numeryczne)
Education = factor(Education, levels = 1:5, labels = c("Średnie i niższe", "Policealne (techniczne)", "Licencjat", "Magister", "Doktor"), ordered = TRUE),
EnvironmentSatisfaction = factor(EnvironmentSatisfaction, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
JobInvolvement = factor(JobInvolvement, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
JobLevel = factor(JobLevel, levels = 1:5, labels = c("Najniższy", "Niski", "Średni", "Wysoki", "Najwyższy"), ordered = TRUE),
JobSatisfaction = factor(JobSatisfaction, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
PerformanceRating = factor(PerformanceRating, levels = 1:4, labels = c("Niska", "Dobra", "Znakomita", "Wybitna"), ordered = TRUE),
RelationshipSatisfaction = factor(RelationshipSatisfaction, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
WorkLifeBalance = factor(WorkLifeBalance, levels = 1:4, labels = c("Zła", "Przeciętna", "Dobra", "Świetna"), ordered = TRUE),
StockOptionLevel = factor(StockOptionLevel, levels = 0:3, labels = c("Brak", "Niski", "Średni", "Wysoki"), ordered = TRUE)
)Graficzną prezentację przekodowanych danych prezentuje poniższy wykres, w celu zwięskzenia czytelności zmienne zostały pogrupowane po typie zmiennej.
3.3. Imputacja danych
W analizie danych zbioru HR istotnym etapem przygotowania danych jest uzupełnienie brakujących wartości. Braki danych (w naszym przypadku 597 wartości) mogą znacząco wpłynąć na wyniki analiz statystycznych i modele predykcyjne, prowadząc do błędnych wniosków. W związku z tym konieczne jest wdrożenie odpowiednich metod imputacji, które pozwolą na kompleksowe uzupełnienie braków, a tym samym na zwiększenie wiarygodności dalszych analiz.
Mechanizm braków w naszym zbiorze danych został określony jako MCAR (Missing Completely at Random), co oznacza, że brakujące dane występują losowo i nie są powiązane z innymi obserwowanymi ani nieobserwowanymi zmiennymi. W celu uwzględnienia charakteru braku danych oraz typu danych w niniejszym rozdziale zaprezentowane zostaną trzy różne podejścia do imputacji:
Metoda k-Najbliższych Sąsiadów (kNN)
Metoda kNN (k-Nearest Neighbours) uzupełnia brakujące dane na podstawie podobieństwa obserwacji. Dla każdej obserwacji z brakującą wartością wyszukiwani są najbliżsi sąsiedzi (na podstawie innych zmiennych) i wartość imputowana jest na podstawie ich danych. Metoda ta jest intuicyjna i dobrze sprawdza się, gdy założymy, że podobne obserwacje mają podobne wartości zmiennych.Metoda Multivariate Imputation by Chained Equations (mice)
mice to zaawansowana technika wielokrotnej imputacji, która pozwala na stworzenie kilku kompletnych zbiorów danych. Metoda ta polega na iteracyjnym uzupełnianiu brakujących wartości przy użyciu modeli statystycznych, takich jak regresja logistyczna dla zmiennych binarnych (Attrition) oraz predictive mean matching (pmm) dla zmiennych ciągłych (MonthlyIncome i Age) lub dyskretnych (NumCompaniesWorked). Dzięki podejściu wielokrotnemu jesteśmy w stanie uwzględnić niepewność imputacji, co przekłada się na bardziej wiarygodne oszacowanie brakujących danych.Metoda drzew decyzyjnych (rpart)
Imputacja przy użyciu drzew decyzyjnych (Recursive Partitioning and Regression Trees) polega na budowie modelu predykcyjnego dla każdej zmiennej z brakami. Dla zmiennych ciągłych stosowana jest regresja drzew decyzyjnych, natomiast dla zmiennych kategorycznych – klasyfikacyjnych. Metoda ta jest szczególnie przydatna, gdy zależności między zmiennymi są nieliniowe lub bardziej złożone. Drzewa decyzyjne potrafią wychwycić te zależności, co pozwala na precyzyjne uzupełnienie brakujących wartości, nawet gdy dane są kompletne zgodnie z założeniem MCAR.
Kryterium decyzyjnym do wyboru zmiennych, na podstawie których imputowane będą wartości metodami mice i rpart, będzie wartość współczynnika korelacji liniowej Pearsona, którego wzór jest następujący:
\[ r_{xy} = \frac{{}\sum_{i=1}^{n} (x_i - \overline{x})(y_i - \overline{y})}{\sqrt{\sum_{i=1}^{n} (x_i - \overline{x})^2 \sum_{i=1}^{n}(y_i - \overline{y})^2}} \]
Wartość współczynnika korelacji mieści się w przedziale domkniętym [−1, 1]. Im większa jego wartość bezwzględna, tym silniejsza jest zależność liniowa między zmiennymi. rxy = 0 oznacza brak liniowej zależności między cechami. rxy = 1 oznacza dokładną dodatnią liniową zależność między cechami, natomiast rxy = -1 oznacza dokładną ujemną liniową zależność między cechami. W celu utworzenia macierzy korelacji zmienne kategorialne przekształcone zostaną na zmienne numeryczne.
3.3.1. Tworzenie macierzy korelacji
numeric_HR <- HR_czyste %>%
mutate(across(where(is.factor), as.numeric)) %>%
select(where(is.numeric))
macierz_kor_numeric <- cor_mat(
numeric_HR,
method = "pearson",
alternative = "two.sided",
conf.level = 0.95)
macierz_kor_numeric_mx <- macierz_kor_numeric %>%
column_to_rownames(var = "rowname") %>%
as.matrix()
macierz_kor_numeric %>%
kable("html", caption = "Macierz korelacji dla zmiennych numerycznych") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>%
scroll_box(width = "100%", height = "300px")| rowname | Age | Attrition | BusinessTravel | DailyRate | Department | DistanceFromHome | Education | EducationField | EnvironmentSatisfaction | Gender | HourlyRate | JobInvolvement | JobLevel | JobRole | JobSatisfaction | MaritalStatus | MonthlyIncome | MonthlyRate | NumCompaniesWorked | OverTime | PercentSalaryHike | PerformanceRating | RelationshipSatisfaction | StockOptionLevel | TotalWorkingYears | TrainingTimesLastYear | WorkLifeBalance | YearsAtCompany | YearsInCurrentRole | YearsSinceLastPromotion | YearsWithCurrManager |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 1.0000 | -0.1700 | -0.01600 | 0.00770 | -0.0410 | -0.0150 | 0.2000 | -0.03100 | 0.00700 | -0.03200 | 0.03100 | 0.0260 | 0.51000 | -0.13000 | -0.00720 | 0.0840 | 0.490 | 0.02800 | 0.3000 | 0.02200 | 0.01300 | 0.00680 | 0.05700 | 0.02900 | 0.6800 | -0.0270 | -0.0310 | 0.3100 | 0.2100 | 0.2200 | 0.21000 |
| Attrition | -0.1700 | 1.0000 | 0.13000 | -0.05700 | 0.0620 | 0.1000 | -0.0330 | 0.00710 | -0.08700 | 0.03100 | 0.00690 | -0.1300 | -0.18000 | 0.06600 | -0.11000 | -0.1500 | -0.160 | 0.00520 | 0.0210 | 0.24000 | -0.01200 | 0.01500 | -0.04000 | -0.14000 | -0.1700 | -0.0600 | -0.0720 | -0.1400 | -0.1700 | -0.0370 | -0.17000 |
| BusinessTravel | -0.0160 | 0.1300 | 1.00000 | -0.01600 | -0.0026 | -0.0097 | -0.0087 | -0.02000 | -0.01100 | -0.04500 | -0.00420 | 0.0290 | -0.01200 | 0.01200 | 0.00870 | -0.0310 | -0.029 | -0.00840 | -0.0330 | 0.04300 | -0.02600 | 0.00170 | 0.00890 | -0.02800 | 0.0080 | 0.0160 | 0.0042 | 0.0052 | -0.0053 | 0.0052 | -0.00023 |
| DailyRate | 0.0077 | -0.0570 | -0.01600 | 1.00000 | 0.0071 | -0.0050 | -0.0170 | 0.03200 | 0.01800 | -0.01200 | 0.02300 | 0.0460 | 0.00300 | -0.00950 | 0.03100 | 0.0700 | 0.020 | -0.03200 | 0.0290 | 0.00910 | 0.02300 | 0.00047 | 0.00780 | 0.04200 | 0.0150 | 0.0025 | -0.0380 | -0.0340 | 0.0099 | -0.0330 | -0.02600 |
| Department | -0.0410 | 0.0620 | -0.00260 | 0.00710 | 1.0000 | 0.0170 | 0.0080 | 0.00930 | -0.01900 | -0.04200 | -0.00410 | -0.0250 | 0.10000 | 0.66000 | 0.02100 | -0.0560 | 0.050 | 0.02400 | -0.0400 | 0.00750 | -0.00780 | -0.02500 | -0.02200 | -0.01200 | -0.0160 | 0.0370 | 0.0260 | 0.0230 | 0.0560 | 0.0400 | 0.03400 |
| DistanceFromHome | -0.0150 | 0.1000 | -0.00970 | -0.00500 | 0.0170 | 1.0000 | 0.0210 | 0.00400 | -0.01600 | -0.00190 | 0.03100 | 0.0088 | 0.00530 | -0.00100 | -0.00370 | 0.0140 | -0.024 | 0.02700 | -0.0510 | 0.02600 | 0.04000 | 0.02700 | 0.00660 | 0.04500 | 0.0046 | -0.0370 | -0.0270 | 0.0095 | 0.0190 | 0.0100 | 0.01400 |
| Education | 0.2000 | -0.0330 | -0.00870 | -0.01700 | 0.0080 | 0.0210 | 1.0000 | -0.02900 | -0.02700 | -0.01700 | 0.01700 | 0.0420 | 0.10000 | 0.00420 | -0.01100 | -0.0041 | 0.095 | -0.02600 | 0.1300 | -0.02000 | -0.01100 | -0.02500 | -0.00910 | 0.01800 | 0.1500 | -0.0250 | 0.0098 | 0.0690 | 0.0600 | 0.0540 | 0.06900 |
| EducationField | -0.0310 | 0.0071 | -0.02000 | 0.03200 | 0.0093 | 0.0040 | -0.0290 | 1.00000 | 0.05100 | 0.00066 | -0.03300 | -0.0039 | -0.03800 | 0.00170 | -0.03100 | -0.0110 | -0.053 | -0.03400 | 0.0140 | 0.01100 | 0.00130 | 0.00110 | -0.00580 | -0.01900 | -0.0250 | 0.0480 | 0.0440 | -0.0200 | -0.0160 | -0.0056 | -0.00620 |
| EnvironmentSatisfaction | 0.0070 | -0.0870 | -0.01100 | 0.01800 | -0.0190 | -0.0160 | -0.0270 | 0.05100 | 1.00000 | 0.00051 | -0.05000 | -0.0083 | 0.00120 | -0.01700 | -0.00680 | 0.0036 | -0.018 | 0.03800 | 0.0170 | 0.07000 | -0.03200 | -0.03000 | 0.00770 | 0.00340 | -0.0027 | -0.0190 | 0.0280 | 0.0015 | 0.0180 | 0.0160 | -0.00500 |
| Gender | -0.0320 | 0.0310 | -0.04500 | -0.01200 | -0.0420 | -0.0019 | -0.0170 | 0.00066 | 0.00051 | 1.00000 | -0.00048 | 0.0180 | -0.03900 | -0.04000 | 0.03300 | 0.0470 | -0.013 | -0.04100 | -0.0320 | -0.04200 | 0.00270 | -0.01400 | 0.02300 | 0.01300 | -0.0470 | -0.0390 | -0.0028 | -0.0300 | -0.0410 | -0.0270 | -0.03100 |
| HourlyRate | 0.0310 | 0.0069 | -0.00420 | 0.02300 | -0.0041 | 0.0310 | 0.0170 | -0.03300 | -0.05000 | -0.00048 | 1.00000 | 0.0430 | -0.02800 | -0.01900 | -0.07100 | 0.0180 | -0.018 | -0.01500 | 0.0240 | -0.00780 | -0.00910 | -0.00220 | 0.00130 | 0.05000 | -0.0023 | -0.0085 | -0.0046 | -0.0200 | -0.0240 | -0.0270 | -0.02000 |
| JobInvolvement | 0.0260 | -0.1300 | 0.02900 | 0.04600 | -0.0250 | 0.0088 | 0.0420 | -0.00390 | -0.00830 | 0.01800 | 0.04300 | 1.0000 | -0.01300 | 0.00660 | -0.02100 | 0.0380 | -0.021 | -0.01600 | 0.0240 | -0.00350 | -0.01700 | -0.02900 | 0.03400 | 0.02200 | -0.0055 | -0.0150 | -0.0150 | -0.0210 | 0.0087 | -0.0240 | 0.02600 |
| JobLevel | 0.5100 | -0.1800 | -0.01200 | 0.00300 | 0.1000 | 0.0053 | 0.1000 | -0.03800 | 0.00120 | -0.03900 | -0.02800 | -0.0130 | 1.00000 | -0.08500 | -0.00190 | 0.0770 | 0.950 | 0.04000 | 0.1400 | 0.00054 | -0.03500 | -0.02100 | 0.02200 | 0.01400 | 0.7800 | -0.0180 | 0.0380 | 0.5300 | 0.3900 | 0.3500 | 0.38000 |
| JobRole | -0.1300 | 0.0660 | 0.01200 | -0.00950 | 0.6600 | -0.0010 | 0.0042 | 0.00170 | -0.01700 | -0.04000 | -0.01900 | 0.0066 | -0.08500 | 1.00000 | 0.01800 | -0.0680 | -0.093 | 0.00530 | -0.0630 | 0.04100 | -0.00085 | -0.02400 | -0.02000 | -0.01900 | -0.1500 | 0.0013 | 0.0280 | -0.0840 | -0.0280 | -0.0460 | -0.04100 |
| JobSatisfaction | -0.0072 | -0.1100 | 0.00870 | 0.03100 | 0.0210 | -0.0037 | -0.0110 | -0.03100 | -0.00680 | 0.03300 | -0.07100 | -0.0210 | -0.00190 | 0.01800 | 1.00000 | -0.0240 | -0.021 | 0.00064 | -0.0590 | 0.02500 | 0.02000 | 0.00230 | -0.01200 | 0.01100 | -0.0200 | -0.0058 | -0.0190 | -0.0038 | -0.0023 | -0.0180 | -0.02800 |
| MaritalStatus | 0.0840 | -0.1500 | -0.03100 | 0.07000 | -0.0560 | 0.0140 | -0.0041 | -0.01100 | 0.00360 | 0.04700 | 0.01800 | 0.0380 | 0.07700 | -0.06800 | -0.02400 | 1.0000 | 0.065 | -0.02400 | 0.0230 | 0.01800 | -0.01200 | -0.00520 | -0.02300 | 0.66000 | 0.0780 | -0.0110 | -0.0150 | 0.0600 | 0.0660 | 0.0310 | 0.03900 |
| MonthlyIncome | 0.4900 | -0.1600 | -0.02900 | 0.02000 | 0.0500 | -0.0240 | 0.0950 | -0.05300 | -0.01800 | -0.01300 | -0.01800 | -0.0210 | 0.95000 | -0.09300 | -0.02100 | 0.0650 | 1.000 | 0.03400 | 0.1400 | 0.01400 | -0.02400 | -0.01400 | 0.03000 | -0.01100 | 0.7700 | -0.0220 | 0.0270 | 0.5100 | 0.3600 | 0.3500 | 0.34000 |
| MonthlyRate | 0.0280 | 0.0052 | -0.00840 | -0.03200 | 0.0240 | 0.0270 | -0.0260 | -0.03400 | 0.03800 | -0.04100 | -0.01500 | -0.0160 | 0.04000 | 0.00530 | 0.00064 | -0.0240 | 0.034 | 1.00000 | 0.0160 | 0.02100 | -0.00640 | -0.00980 | -0.00410 | -0.03400 | 0.0260 | 0.0015 | 0.0080 | -0.0240 | -0.0130 | 0.0016 | -0.03700 |
| NumCompaniesWorked | 0.3000 | 0.0210 | -0.03300 | 0.02900 | -0.0400 | -0.0510 | 0.1300 | 0.01400 | 0.01700 | -0.03200 | 0.02400 | 0.0240 | 0.14000 | -0.06300 | -0.05900 | 0.0230 | 0.140 | 0.01600 | 1.0000 | -0.02900 | -0.00670 | -0.02600 | 0.04600 | 0.02400 | 0.2300 | -0.0670 | -0.0230 | -0.1000 | -0.0780 | -0.0350 | -0.09300 |
| OverTime | 0.0220 | 0.2400 | 0.04300 | 0.00910 | 0.0075 | 0.0260 | -0.0200 | 0.01100 | 0.07000 | -0.04200 | -0.00780 | -0.0035 | 0.00054 | 0.04100 | 0.02500 | 0.0180 | 0.014 | 0.02100 | -0.0290 | 1.00000 | -0.00540 | 0.00440 | 0.04800 | -0.00045 | 0.0130 | -0.0790 | -0.0270 | -0.0120 | -0.0300 | -0.0120 | -0.04200 |
| PercentSalaryHike | 0.0130 | -0.0120 | -0.02600 | 0.02300 | -0.0078 | 0.0400 | -0.0110 | 0.00130 | -0.03200 | 0.00270 | -0.00910 | -0.0170 | -0.03500 | -0.00085 | 0.02000 | -0.0120 | -0.024 | -0.00640 | -0.0067 | -0.00540 | 1.00000 | 0.77000 | -0.04000 | 0.00750 | -0.0210 | -0.0052 | -0.0033 | -0.0360 | -0.0015 | -0.0220 | -0.01200 |
| PerformanceRating | 0.0068 | 0.0150 | 0.00170 | 0.00047 | -0.0250 | 0.0270 | -0.0250 | 0.00110 | -0.03000 | -0.01400 | -0.00220 | -0.0290 | -0.02100 | -0.02400 | 0.00230 | -0.0052 | -0.014 | -0.00980 | -0.0260 | 0.00440 | 0.77000 | 1.00000 | -0.03100 | 0.00350 | 0.0067 | -0.0160 | 0.0026 | 0.0034 | 0.0350 | 0.0180 | 0.02300 |
| RelationshipSatisfaction | 0.0570 | -0.0400 | 0.00890 | 0.00780 | -0.0220 | 0.0066 | -0.0091 | -0.00580 | 0.00770 | 0.02300 | 0.00130 | 0.0340 | 0.02200 | -0.02000 | -0.01200 | -0.0230 | 0.030 | -0.00410 | 0.0460 | 0.04800 | -0.04000 | -0.03100 | 1.00000 | -0.04600 | 0.0240 | 0.0025 | 0.0200 | 0.0190 | -0.0150 | 0.0330 | -0.00087 |
| StockOptionLevel | 0.0290 | -0.1400 | -0.02800 | 0.04200 | -0.0120 | 0.0450 | 0.0180 | -0.01900 | 0.00340 | 0.01300 | 0.05000 | 0.0220 | 0.01400 | -0.01900 | 0.01100 | 0.6600 | -0.011 | -0.03400 | 0.0240 | -0.00045 | 0.00750 | 0.00350 | -0.04600 | 1.00000 | 0.0100 | 0.0110 | 0.0041 | 0.0150 | 0.0510 | 0.0140 | 0.02500 |
| TotalWorkingYears | 0.6800 | -0.1700 | 0.00800 | 0.01500 | -0.0160 | 0.0046 | 0.1500 | -0.02500 | -0.00270 | -0.04700 | -0.00230 | -0.0055 | 0.78000 | -0.15000 | -0.02000 | 0.0780 | 0.770 | 0.02600 | 0.2300 | 0.01300 | -0.02100 | 0.00670 | 0.02400 | 0.01000 | 1.0000 | -0.0360 | 0.0010 | 0.6300 | 0.4600 | 0.4000 | 0.46000 |
| TrainingTimesLastYear | -0.0270 | -0.0600 | 0.01600 | 0.00250 | 0.0370 | -0.0370 | -0.0250 | 0.04800 | -0.01900 | -0.03900 | -0.00850 | -0.0150 | -0.01800 | 0.00130 | -0.00580 | -0.0110 | -0.022 | 0.00150 | -0.0670 | -0.07900 | -0.00520 | -0.01600 | 0.00250 | 0.01100 | -0.0360 | 1.0000 | 0.0280 | 0.0036 | -0.0057 | -0.0021 | -0.00410 |
| WorkLifeBalance | -0.0310 | -0.0720 | 0.00420 | -0.03800 | 0.0260 | -0.0270 | 0.0098 | 0.04400 | 0.02800 | -0.00280 | -0.00460 | -0.0150 | 0.03800 | 0.02800 | -0.01900 | -0.0150 | 0.027 | 0.00800 | -0.0230 | -0.02700 | -0.00330 | 0.00260 | 0.02000 | 0.00410 | 0.0010 | 0.0280 | 1.0000 | 0.0120 | 0.0500 | 0.0089 | 0.00280 |
| YearsAtCompany | 0.3100 | -0.1400 | 0.00520 | -0.03400 | 0.0230 | 0.0095 | 0.0690 | -0.02000 | 0.00150 | -0.03000 | -0.02000 | -0.0210 | 0.53000 | -0.08400 | -0.00380 | 0.0600 | 0.510 | -0.02400 | -0.1000 | -0.01200 | -0.03600 | 0.00340 | 0.01900 | 0.01500 | 0.6300 | 0.0036 | 0.0120 | 1.0000 | 0.7600 | 0.6200 | 0.77000 |
| YearsInCurrentRole | 0.2100 | -0.1700 | -0.00530 | 0.00990 | 0.0560 | 0.0190 | 0.0600 | -0.01600 | 0.01800 | -0.04100 | -0.02400 | 0.0087 | 0.39000 | -0.02800 | -0.00230 | 0.0660 | 0.360 | -0.01300 | -0.0780 | -0.03000 | -0.00150 | 0.03500 | -0.01500 | 0.05100 | 0.4600 | -0.0057 | 0.0500 | 0.7600 | 1.0000 | 0.5500 | 0.71000 |
| YearsSinceLastPromotion | 0.2200 | -0.0370 | 0.00520 | -0.03300 | 0.0400 | 0.0100 | 0.0540 | -0.00560 | 0.01600 | -0.02700 | -0.02700 | -0.0240 | 0.35000 | -0.04600 | -0.01800 | 0.0310 | 0.350 | 0.00160 | -0.0350 | -0.01200 | -0.02200 | 0.01800 | 0.03300 | 0.01400 | 0.4000 | -0.0021 | 0.0089 | 0.6200 | 0.5500 | 1.0000 | 0.51000 |
| YearsWithCurrManager | 0.2100 | -0.1700 | -0.00023 | -0.02600 | 0.0340 | 0.0140 | 0.0690 | -0.00620 | -0.00500 | -0.03100 | -0.02000 | 0.0260 | 0.38000 | -0.04100 | -0.02800 | 0.0390 | 0.340 | -0.03700 | -0.0930 | -0.04200 | -0.01200 | 0.02300 | -0.00087 | 0.02500 | 0.4600 | -0.0041 | 0.0028 | 0.7700 | 0.7100 | 0.5100 | 1.00000 |
3.3.2. Macierz korelacji zmiennych przed imputacją
corrplot(
macierz_kor_numeric_mx,
method = "color",
type = "upper",
col = colorRampPalette(c("#4477AA", "white", "#BB4444"))(200),
insig = "blank",
tl.cex = 0.9
)
## Age
## TotalWorkingYears 0.6800
## JobLevel 0.5100
## MonthlyIncome 0.4900
## YearsAtCompany 0.3100
## NumCompaniesWorked 0.3000
## YearsSinceLastPromotion 0.2200
## YearsInCurrentRole 0.2100
## YearsWithCurrManager 0.2100
## Education 0.2000
## Attrition -0.1700
## JobRole -0.1300
## MaritalStatus 0.0840
## RelationshipSatisfaction 0.0570
## Department -0.0410
## Gender -0.0320
## EducationField -0.0310
## HourlyRate 0.0310
## WorkLifeBalance -0.0310
## StockOptionLevel 0.0290
## MonthlyRate 0.0280
## TrainingTimesLastYear -0.0270
## JobInvolvement 0.0260
## OverTime 0.0220
## BusinessTravel -0.0160
## DistanceFromHome -0.0150
## PercentSalaryHike 0.0130
## DailyRate 0.0077
## JobSatisfaction -0.0072
## EnvironmentSatisfaction 0.0070
## PerformanceRating 0.0068## Attrition
## OverTime 0.2400
## JobLevel -0.1800
## Age -0.1700
## TotalWorkingYears -0.1700
## YearsInCurrentRole -0.1700
## YearsWithCurrManager -0.1700
## MonthlyIncome -0.1600
## MaritalStatus -0.1500
## StockOptionLevel -0.1400
## YearsAtCompany -0.1400
## BusinessTravel 0.1300
## JobInvolvement -0.1300
## JobSatisfaction -0.1100
## DistanceFromHome 0.1000
## EnvironmentSatisfaction -0.0870
## WorkLifeBalance -0.0720
## JobRole 0.0660
## Department 0.0620
## TrainingTimesLastYear -0.0600
## DailyRate -0.0570
## RelationshipSatisfaction -0.0400
## YearsSinceLastPromotion -0.0370
## Education -0.0330
## Gender 0.0310
## NumCompaniesWorked 0.0210
## PerformanceRating 0.0150
## PercentSalaryHike -0.0120
## EducationField 0.0071
## HourlyRate 0.0069
## MonthlyRate 0.0052## MonthlyIncome
## JobLevel 0.950
## TotalWorkingYears 0.770
## YearsAtCompany 0.510
## Age 0.490
## YearsInCurrentRole 0.360
## YearsSinceLastPromotion 0.350
## YearsWithCurrManager 0.340
## Attrition -0.160
## NumCompaniesWorked 0.140
## Education 0.095
## JobRole -0.093
## MaritalStatus 0.065
## EducationField -0.053
## Department 0.050
## MonthlyRate 0.034
## RelationshipSatisfaction 0.030
## BusinessTravel -0.029
## WorkLifeBalance 0.027
## DistanceFromHome -0.024
## PercentSalaryHike -0.024
## TrainingTimesLastYear -0.022
## JobInvolvement -0.021
## JobSatisfaction -0.021
## DailyRate 0.020
## EnvironmentSatisfaction -0.018
## HourlyRate -0.018
## OverTime 0.014
## PerformanceRating -0.014
## Gender -0.013
## StockOptionLevel -0.011## NumCompaniesWorked
## Age 0.3000
## TotalWorkingYears 0.2300
## JobLevel 0.1400
## MonthlyIncome 0.1400
## Education 0.1300
## YearsAtCompany -0.1000
## YearsWithCurrManager -0.0930
## YearsInCurrentRole -0.0780
## TrainingTimesLastYear -0.0670
## JobRole -0.0630
## JobSatisfaction -0.0590
## DistanceFromHome -0.0510
## RelationshipSatisfaction 0.0460
## Department -0.0400
## YearsSinceLastPromotion -0.0350
## BusinessTravel -0.0330
## Gender -0.0320
## DailyRate 0.0290
## OverTime -0.0290
## PerformanceRating -0.0260
## HourlyRate 0.0240
## JobInvolvement 0.0240
## StockOptionLevel 0.0240
## MaritalStatus 0.0230
## WorkLifeBalance -0.0230
## Attrition 0.0210
## EnvironmentSatisfaction 0.0170
## MonthlyRate 0.0160
## EducationField 0.0140
## PercentSalaryHike -0.0067Na podstawie macierzy korelacji zauważamy:
- zmienna Age skorelowana jest najsilniej z trzema zmiennymi - TotalWorkingYears (0.68), JobLevel (0.51) oraz MonthlyIncome (0.49);
- MonthlyIncome ze zmiennymi JobLevel (0.95), TotalWorkingYears (0.77), YearsAtCompany (0.51) oraz Age (0.49);
- zmienne Attrition oraz NumCompaniesWorked natomiast nie sa zbyt mocno skorelowana z żadną ze zmiennych, których to wartości absolutne znajdują się w przedziale kolejno [0,0052 - 0,24] oraz [0.0067 - 0,30]. Przyjmiemy tym samym do imputacji wszystkie możliwe zmienne numeryczne, pomijając zmienne, w których występują braki.
Wybór zmiennych do imputacji prezentuje poniższy kod.
napdst_Age <- c("TotalWorkingYears", "JobLevel", "MonthlyIncome")
napdst_Attrition <- c("DailyRate", "DistanceFromHome", "Education", "EnvironmentSatisfaction",
"HourlyRate", "JobInvolvement", "JobLevel", "JobSatisfaction",
"PercentSalaryHike", "PerformanceRating",
"RelationshipSatisfaction", "StockOptionLevel", "TotalWorkingYears",
"TrainingTimesLastYear", "WorkLifeBalance", "YearsAtCompany",
"YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager")
napdst_MonthlyIncome <- c("JobLevel", "TotalWorkingYears", "YearsAtCompany", "Age")
napdst_NumCompaniesWorked <- c("DailyRate", "DistanceFromHome", "Education", "EnvironmentSatisfaction",
"HourlyRate", "JobInvolvement", "JobLevel", "JobSatisfaction",
"PercentSalaryHike", "PerformanceRating",
"RelationshipSatisfaction", "StockOptionLevel", "TotalWorkingYears",
"TrainingTimesLastYear", "WorkLifeBalance", "YearsAtCompany",
"YearsInCurrentRole", "YearsSinceLastPromotion", "YearsWithCurrManager")imputed_data <- HR_czyste %>%
mutate(
Department = as.numeric(Department),
EducationField = as.numeric(EducationField),
Gender = as.numeric(Gender),
JobRole = as.numeric(JobRole),
MaritalStatus = as.numeric(MaritalStatus),
Attrition = as.numeric(Attrition),
BusinessTravel = as.numeric(BusinessTravel),
OverTime = as.numeric(OverTime),
Education = as.numeric(Education),
EnvironmentSatisfaction = as.numeric(EnvironmentSatisfaction),
JobInvolvement = as.numeric(JobInvolvement),
JobLevel = as.numeric(JobLevel),
JobSatisfaction = as.numeric(JobSatisfaction),
PerformanceRating = as.numeric(PerformanceRating),
RelationshipSatisfaction = as.numeric(RelationshipSatisfaction),
WorkLifeBalance = as.numeric(WorkLifeBalance),
StockOptionLevel = as.numeric(StockOptionLevel)
)
imputed_data_c <- imputed_dataimputed_data <- imputed_data %>%
mutate(
Age_knn = round_half_up(kNN(imputed_data_c, variable = "Age")$Age),
Age_mice = round_half_up(imputate_na(imputed_data_c, xvar = "Age", yvar = napdst_Age, method = "mice", print_flag = FALSE)),
Age_rpart = round_half_up(imputate_na(imputed_data_c, xvar = "Age", yvar = napdst_Age, method = "rpart")),
Attrition_knn = round_half_up(kNN(imputed_data_c, variable = "Attrition")$Attrition),
Attrition_mice = round_half_up(imputate_na(imputed_data_c, xvar = "Attrition", yvar = napdst_Attrition, method = "mice", print_flag = FALSE)),
Attrition_rpart = round_half_up(imputate_na(imputed_data_c, xvar = "Attrition", yvar = napdst_Attrition, method = "rpart")),
MonthlyIncome_knn = round_half_up(kNN(imputed_data_c, variable = "MonthlyIncome")$MonthlyIncome),
MonthlyIncome_mice = round_half_up(imputate_na(imputed_data_c, xvar = "MonthlyIncome", yvar = napdst_MonthlyIncome, method = "mice", print_flag = FALSE)),
MonthlyIncome_rpart = round_half_up(imputate_na(imputed_data_c, xvar = "MonthlyIncome", yvar = napdst_MonthlyIncome, method = "rpart")),
NumCompaniesWorked_knn = round_half_up(kNN(imputed_data_c, variable = "NumCompaniesWorked")$NumCompaniesWorked),
NumCompaniesWorked_mice = round_half_up(imputate_na(imputed_data_c, xvar = "NumCompaniesWorked", yvar = napdst_NumCompaniesWorked, method = "mice", print_flag = FALSE)),
NumCompaniesWorked_rpart = round_half_up(imputate_na(imputed_data_c, xvar = "NumCompaniesWorked", yvar = napdst_NumCompaniesWorked, method = "rpart"))
)create_plot <- function(data, original, imputed, title) {
ggplot2::ggplot(data) +
ggplot2::geom_density(ggplot2::aes(x = {{ original }}), color = "#96d170", alpha = 0.4) +
ggplot2::geom_density(ggplot2::aes(x = {{ imputed }}), color = "#cb8873", alpha = 0.4) +
ggplot2::labs(title = title, x = "Wartość", y = "Gęstość rozkładu") +
ggplot2::theme_minimal()
}
plot_age_knn <- create_plot(imputed_data, Age, Age_knn, "Age: kNN")
plot_age_mice <- create_plot(imputed_data, Age, Age_mice, "Age: mice")
plot_age_rpart <- create_plot(imputed_data, Age, Age_rpart, "Age: rpart")
plot_attrition_knn <- create_plot(imputed_data, Attrition, Attrition_knn, "Attrition: kNN")
plot_attrition_mice <- create_plot(imputed_data, Attrition, Attrition_mice, "Attrition: mice")
plot_attrition_rpart <- create_plot(imputed_data, Attrition, Attrition_rpart, "Attrition: rpart")
plot_income_knn <- create_plot(imputed_data, MonthlyIncome, MonthlyIncome_knn, "MonthlyIncome: kNN")
plot_income_mice <- create_plot(imputed_data, MonthlyIncome, MonthlyIncome_mice, "MonthlyIncome: mice")
plot_income_rpart <- create_plot(imputed_data, MonthlyIncome, MonthlyIncome_rpart, "MonthlyIncome: rpart")
plot_ncw_knn <- create_plot(imputed_data, NumCompaniesWorked, NumCompaniesWorked_knn, "NumCompaniesWorked: kNN")
plot_ncw_mice <- create_plot(imputed_data, NumCompaniesWorked, NumCompaniesWorked_mice, "NumCompaniesWorked: mice")
plot_ncw_rpart <- create_plot(imputed_data, NumCompaniesWorked, NumCompaniesWorked_rpart, "NumCompaniesWorked: rpart")
combined_age_plots <- patchwork::wrap_plots(plot_age_knn, plot_age_mice, plot_age_rpart, nrow = 1)
combined_attrition_plots <- patchwork::wrap_plots(plot_attrition_knn, plot_attrition_mice, plot_attrition_rpart, nrow = 1)
combined_income_plots <- patchwork::wrap_plots(plot_income_knn, plot_income_mice, plot_income_rpart, nrow = 1)
combined_ncw_plots <- patchwork::wrap_plots(plot_ncw_knn, plot_ncw_mice, plot_ncw_rpart, nrow = 1)3.4. Porównianie metod imputacji dla poszczególnych zmiennych
3.5. Uzupełnianie braków danych
Na podstawie wizualnej oceny rozkładu zmiennych oryginalnych względem imputowanych przy wykorzystaniu metod kNN, mice oraz rpart, jak również biorąc pod uwagę typ braków MCAR i zaobserwowanych w rodziale 2.3. zależności, uznano, iż wypełnienie braków zmiennych wykonane zostanie odpowiednio dla zmiennych:
- Age - metodą mice, jako uwzględniającej najlepiej uwzględniającej silne zalezności z niektórymi zmiennymi, oraz dającej wartości najbardziej zbliżone do wartości na wykresie rozrzutu;
- Attrition - metodą rpart, jako najbardziej zbliżoną do oryginalnego rozkładu, gdyż najlepiej radzi sobie ze słabymi i nieliniowmi zależnościami;
- MonthlyIncome - metodą mice, z tych samych powodów, dla których wybrano tą metodę do zmiennej Age;
- NumCompaniesWorked - metodą kNN, gdyż cechuje się ona najbardziej zbliżonym rozkładem do originalnego, a sama zmienna nie wykazywała szczególnej zależności od innych zmienych.
HR_imputowane <- numeric_HR %>%
mutate(
Age = as.numeric(imputed_data$Age_mice),
Attrition = as.numeric(imputed_data$Attrition_rpart),
MonthlyIncome = as.numeric(imputed_data$MonthlyIncome_mice),
NumCompaniesWorked = as.numeric(imputed_data$NumCompaniesWorked_knn)
) %>%
mutate(
# przy rekodowaniu wartości kategorialnych na numeric, jeżeli wartości "zaczynały się od 0", nastąpiło przesuniecie o 1
# poniższy kod naprawia to
Attrition = Attrition - 1,
Gender = Gender - 1,
BusinessTravel = BusinessTravel - 1,
OverTime = OverTime - 1,
StockOptionLevel = StockOptionLevel - 1
) %>%
mutate(
# Zmienne kategorialne nieuporządkowane
Department = factor(Department, levels = 1:3, labels = c("Kadry", "Badania i Rozwój", "Sprzedaż"), ordered = FALSE),
EducationField = factor(EducationField, levels = 1:6, labels = c("Zarządzanie Zasobami Ludzkimi", "Nauki Przyrodnicze", "Marketing", "Medyczne", "Techniczne", "Inne"), ordered = FALSE),
Gender = factor(Gender, levels = 0:1, labels = c("Kobieta", "Mężczyzna"), ordered = FALSE),
JobRole = factor(JobRole, levels = 1:9, labels = c("Przedstawiciel Medyczny", "Pracownik Działu Kadr", "Technik Laboratoryjny", "Menedżer", "Dyrektor Produkcji", "Dyrektor Badań", "Pracownik Działu Badań", "Dyrektor Sprzedaży", "Przedstawiciel Handlowy"), ordered = FALSE),
MaritalStatus = factor(MaritalStatus, levels = 1:3, labels = c("Panna/Kawaler", "Żonata/y", "Rozwiedziona/y"), ordered = FALSE),
# Zmienne kategorialne uporządkowane
Attrition = factor(Attrition, levels = 0:1, labels = c("Nie", "Tak"), ordered = TRUE),
BusinessTravel = factor(BusinessTravel, levels = 0:2, labels = c("Brak", "Rzadko", "Często"), ordered = TRUE),
OverTime = factor(OverTime, levels = 0:1, labels = c("Nie", "Tak"), ordered = TRUE),
# Zmienne kategorialne uporządkowane
Education = factor(Education, levels = 1:5, labels = c("Średnie i niższe", "Policealne (techniczne)", "Licencjat", "Magister", "Doktor"), ordered = TRUE),
EnvironmentSatisfaction = factor(EnvironmentSatisfaction, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
JobInvolvement = factor(JobInvolvement, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
JobLevel = factor(JobLevel, levels = 1:5, labels = c("Najniższy", "Niski", "Średni", "Wysoki", "Najwyższy"), ordered = TRUE),
JobSatisfaction = factor(JobSatisfaction, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
PerformanceRating = factor(PerformanceRating, levels = 1:4, labels = c("Niska", "Dobra", "Znakomita", "Wybitna"), ordered = TRUE),
RelationshipSatisfaction = factor(RelationshipSatisfaction, levels = 1:4, labels = c("Niskie", "Średnie", "Wysokie", "Bardzo wysokie"), ordered = TRUE),
WorkLifeBalance = factor(WorkLifeBalance, levels = 1:4, labels = c("Zła", "Przeciętna", "Dobra", "Świetna"), ordered = TRUE),
StockOptionLevel = factor(StockOptionLevel, levels = 0:3, labels = c("Brak", "Niski", "Średni", "Wysoki"), ordered = TRUE)
)
HR_final <- HR_imputowane %>%
clean_names()
Zauważyć mozemy, iż imputacja danych powiodła się. Zmienne posiadają
ten sam typ zmiennych, różnice występują wyłącznie w wartościach, które
zostały uzupełnione względem wstępnie oczyszczonych danych
HR_clean.
4. Analiza wartości odstających
4.1 Identyfikacja wartości odstających
W celu sprawdzenia w jakich zmiennych występują wartości odstające zastosowano zdefiniowaną poniżej funcję, aby znaleźć zmienne zawierające wartości odstające.
Dzięki temu będziemy mogli potem podzielić klientów na tych zarabiających przeciętnie oraz tych, którzy zarabiają ponadprzecietnie.
ggplot(HR_final, aes(x = total_working_years, y = job_role, fill = attrition)) +
geom_boxplot() +
labs(x = "Całkowite lata pracy", y = "Stanowisko", title = "Rozkład lat pracy według odejść i pełnionych stanowisk") +
scale_fill_manual(values = c("Tak" = "salmon", "Nie" = "mediumaquamarine"), name = "Odejście") +
theme_minimal()
5. Wizualizacje danych
5.1. Relacja pomiędzy zarobkami, wiekiem i odejściami
ggplot(data = HR_final, aes(x = age, y = monthly_income, color = attrition, group = attrition)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE, na.rm = TRUE) +
scale_color_paletteer_d("khroma::bright", name = "Odejścia", direction = 1, na.value = "black") +
labs(x = "Wiek", y = "Miesięczne zarobki", color = "Odejścia") +
theme_minimal() +
theme(legend.position = "bottom")
Możemy zauważyć, że zmienne monthly_income oraz age wykazują dodatnią korelację – wraz ze wzrostem wieku miesięczney dochód rośnie. Linia trendu potwierdza tę zależność, sugerując, że starsi pracownicy generalnie osiągają wyższe dochody. Sugeruje to powiązanie stażu pracy, a tym samym doświadczenia, z wynagrodzeniem.
Dodatkowo zauważyć należy, iż pracownicy, którzy odeszli z firmy są przeciętnie młodsi oraz zarabiali mniej w porównaniu do pracowników, którzy nie odeszli z przedsiębiorstwa.
5.2. Średni miesięczy dochód według stanowiska
HR_final %>%
select(job_role, monthly_income) %>%
group_by(job_role) %>%
summarize(mean = round(mean(monthly_income), 1)) %>%
ggplot(aes(x = reorder(job_role, -mean), y = mean, fill = mean)) +
geom_bar(stat = "identity", width = 0.9, color = "black") +
geom_text(aes(label = mean), size = 4, vjust = 1.5, color = "#000000") +
scale_fill_gradient(low = "#c8e9de", high = "#D85F60") +
theme_minimal() +
#ggtitle("Średni miesięczny dochód według stanowiska") +
theme(
plot.title = element_text(face = "bold", hjust = 0.5, size = 16),
axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 14)
) +
labs(x = "Stanowisko", y = "Miesięczne dochody", fill = "Średnia") +
scale_y_continuous(labels = scales::comma)
Zauważyć można, że najlepiej zarabiającymi pracownikami są osoby na stanowiskach kierowniczych, najmniej zarabiają natomiast pracownicy najniższego szczebla, zwłaszcza z działów Sprzedaży ora Badań i Rozwoju.
5.3. Stan cywilny a odejścia z przedsiebiorstwa
HR_final %>% group_by(attrition, marital_status) %>% summarize(N = n()) %>% mutate(countT = sum(N)) %>%
group_by(attrition, marital_status, add=TRUE) %>% mutate(per=paste0(round(100*N/countT,1),'%')) %>%
ggplot(aes(x=attrition, y=N, fill=marital_status)) +
geom_bar(stat="identity", position=position_dodge()) +
theme_minimal() +
scale_fill_brewer(palette="Purples") +
geom_text(aes(label = per), size = 4, vjust = 1.2, color = "black", position = position_dodge(0.9)) +
#ggtitle("Stan cywilny a rotacja pracowników") +
theme(plot.title = element_text(face = "bold", hjust = 0.5, size = 15)) +
labs(x = "Odejścia", y = "Liczba pracowników", fill = "Stan cywilny")
Zauważyć można, że z pracy najczęściej odchodzą osoby nie będące w związku małżeńskim. Stanowią oni 48,7% wszystkich, którzy odeszli z przedsiębiorstwa. Najrzadziej odchodzą jednak osoby rozwiedzione (16.4% wszystkich odejść).
Jednocześnie nazleży zauważyć, że zdecydowana większość pracowników pozostających w przedsiębiorstwie jest w związku małżeńskim, co sugerować może, iż potencjalna niechęć do zmiany miejsca zatrudniania podyktowana jest chęcią zachowania stabilnego źródła utrzymania gospodarstwa domowego.
5.4. Równowaga czasu pracy oraz dział przedsiębiorstwa
HR_final %>%
filter(attrition == "Tak") %>%
select(department, work_life_balance) %>%
group_by(department, work_life_balance) %>%
summarize(count = n()) %>%
ggplot(aes(x = fct_relevel(work_life_balance, "Bardzo dobre", "Dobre", "Średnie", "Złe"),
y = count, fill = department)) +
geom_bar(stat = "identity") +
facet_wrap(~department) +
theme_minimal() +
theme(
legend.position = "none",
plot.title = element_blank(),
legend.key = element_rect(fill = "white", colour = "black")
) +
scale_fill_manual(values = c("#59e259", "#da5757", "#7979f3")) +
geom_label(aes(label = count, fill = department), colour = "white", fontface = "italic") +
labs(
y = "Liczba pracowników",
x = "Równowaga pomiędzy życiem prywatnym i zawodowym"
)
Powyższyzy wykres prezntuje liczbę pracowników, który odeszli z przedsiebiorstwa w podziale na dział przedsiebiorstwa (department) oraz na ocenę pracownika co do równowagi między życiem zawodowym a prywatnym (work_life_balance). Zauważyć możemy, iż największą liczbą odejść cechował się Dział Badań i Rozwoju, a najmniejszą Dział Kadr. W kontekście rodziału 5.2. sugerować to może wpływ relatynie niższych zarobków na odejścia pracowników Działu Badań i Rozwoju, niewykluczając jednocześnie innych czynników, takich jak aspiracje zawodowe oraz chęć zdobycia nowych umiejętności i wiedzy, .
Zauważyć można ponadto, iż najczęściej odchodzą pracownicy, którym udaje się pogodzić obowiązki pracownicze z życiem prywatnym, co sugeruje, że pracownicy nie odchodzą z przedsiębiorstwa ze względu na nadmiar obowiązków lub inne wymagania związane z czasem pracy, lecz istnieją inne przyczyny.
6.3. Analiza opisowa
6.3.1. Statystyki opisowe
describe(HR_final) %>%
kable("html", caption = "Statystyki opisowe dla zbioru danych HR") %>%
kable_styling(bootstrap_options = c("striped", "hover"))| described_variables | n | na | mean | sd | se_mean | IQR | skewness | kurtosis | p00 | p01 | p05 | p10 | p20 | p25 | p30 | p40 | p50 | p60 | p70 | p75 | p80 | p90 | p95 | p99 | p100 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1470 | 0 | 36.98707 | 8.94765 | 0.233373 | 13.00 | 0.424668 | -0.336695 | 18 | 20.00 | 24.00 | 26.0 | 29.0 | 30.00 | 32.0 | 34.0 | 36.0 | 38.0 | 41.0 | 43.00 | 45.0 | 50.0 | 54.0 | 58.31 | 60 |
| daily_rate | 1470 | 0 | 802.48571 | 403.50910 | 10.524335 | 692.00 | -0.003519 | -1.203823 | 102 | 117.00 | 165.35 | 242.8 | 391.8 | 465.00 | 530.7 | 656.2 | 802.0 | 942.4 | 1094.6 | 1157.00 | 1224.2 | 1356.0 | 1424.1 | 1485.00 | 1499 |
| distance_from_home | 1470 | 0 | 9.19252 | 8.10686 | 0.211443 | 12.00 | 0.958118 | -0.224833 | 1 | 1.00 | 1.00 | 1.0 | 2.0 | 2.00 | 3.0 | 5.0 | 7.0 | 9.0 | 11.0 | 14.00 | 17.0 | 23.0 | 26.0 | 29.00 | 29 |
| hourly_rate | 1470 | 0 | 65.89116 | 20.32943 | 0.530233 | 35.75 | -0.032311 | -1.196398 | 30 | 30.00 | 33.00 | 38.0 | 45.0 | 48.00 | 52.0 | 59.0 | 66.0 | 73.0 | 80.0 | 83.75 | 87.0 | 94.0 | 97.0 | 100.00 | 100 |
| monthly_income | 1470 | 0 | 6533.46531 | 4601.17378 | 120.007937 | 5469.00 | 1.357991 | 1.064009 | 1051 | 1504.39 | 2141.90 | 2341.9 | 2740.4 | 2976.25 | 3430.3 | 4289.0 | 5015.5 | 5946.8 | 7284.5 | 8445.25 | 9742.4 | 13610.1 | 17545.4 | 19617.03 | 19973 |
| monthly_rate | 1470 | 0 | 14313.10340 | 7117.78604 | 185.646285 | 12414.50 | 0.018578 | -1.214956 | 2094 | 2325.07 | 3384.55 | 4603.0 | 6887.4 | 8047.00 | 9255.7 | 11773.0 | 14235.5 | 16714.2 | 19376.0 | 20461.50 | 21712.0 | 24001.7 | 25431.9 | 26704.24 | 26999 |
| num_companies_worked | 1470 | 0 | 3.01361 | 2.34059 | 0.061047 | 3.00 | 1.050540 | 0.041995 | 1 | 1.00 | 1.00 | 1.0 | 1.0 | 1.00 | 1.0 | 1.0 | 2.0 | 3.0 | 4.0 | 4.00 | 5.0 | 7.0 | 8.0 | 9.00 | 9 |
| percent_salary_hike | 1470 | 0 | 15.20952 | 3.65994 | 0.095459 | 6.00 | 0.821128 | -0.300598 | 11 | 11.00 | 11.00 | 11.0 | 12.0 | 12.00 | 13.0 | 13.0 | 14.0 | 15.0 | 17.0 | 18.00 | 19.0 | 21.0 | 22.0 | 25.00 | 25 |
| total_working_years | 1470 | 0 | 11.27959 | 7.78078 | 0.202939 | 9.00 | 1.117172 | 0.918270 | 0 | 1.00 | 1.00 | 3.0 | 5.0 | 6.00 | 6.7 | 8.0 | 10.0 | 10.0 | 13.0 | 15.00 | 17.0 | 23.0 | 28.0 | 35.00 | 40 |
| training_times_last_year | 1470 | 0 | 2.79932 | 1.28927 | 0.033627 | 1.00 | 0.553124 | 0.494993 | 0 | 0.00 | 1.00 | 2.0 | 2.0 | 2.00 | 2.0 | 2.0 | 3.0 | 3.0 | 3.0 | 3.00 | 4.0 | 5.0 | 5.0 | 6.00 | 6 |
| years_at_company | 1470 | 0 | 7.00816 | 6.12652 | 0.159792 | 6.00 | 1.764529 | 3.935509 | 0 | 0.00 | 1.00 | 1.0 | 2.0 | 3.00 | 3.0 | 5.0 | 5.0 | 7.0 | 9.0 | 9.00 | 10.0 | 15.0 | 20.0 | 31.00 | 40 |
| years_in_current_role | 1470 | 0 | 4.22925 | 3.62314 | 0.094499 | 5.00 | 0.917363 | 0.477421 | 0 | 0.00 | 0.00 | 0.0 | 1.0 | 2.00 | 2.0 | 2.0 | 3.0 | 4.0 | 7.0 | 7.00 | 7.0 | 9.0 | 11.0 | 15.00 | 18 |
| years_since_last_promotion | 1470 | 0 | 2.18776 | 3.22243 | 0.084048 | 3.00 | 1.984290 | 3.612673 | 0 | 0.00 | 0.00 | 0.0 | 0.0 | 0.00 | 0.0 | 1.0 | 1.0 | 1.0 | 2.0 | 3.00 | 4.0 | 7.0 | 9.0 | 14.00 | 15 |
| years_with_curr_manager | 1470 | 0 | 4.12313 | 3.56814 | 0.093064 | 5.00 | 0.833451 | 0.171058 | 0 | 0.00 | 0.00 | 0.0 | 1.0 | 2.00 | 2.0 | 2.0 | 3.0 | 4.0 | 7.0 | 7.00 | 7.0 | 9.0 | 10.0 | 14.00 | 17 |
6.3.2. Korelacja zmiennych
numeric_HR_fin <- HR_final %>%
mutate(across(where(is.factor), as.numeric)) %>%
select(where(is.numeric))
macierz_kor_numeric_fin <- cor_mat(
numeric_HR_fin,
method = "pearson",
alternative = "two.sided",
conf.level = 0.95)
macierz_kor_numeric_mx_fin <- macierz_kor_numeric_fin %>%
column_to_rownames(var = "rowname") %>%
as.matrix()
macierz_kor_numeric_fin %>%
kable("html", caption = "Macierz korelacji dla zmiennych numerycznych") %>%
kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>%
scroll_box(width = "100%", height = "300px")| rowname | age | attrition | business_travel | daily_rate | department | distance_from_home | education | education_field | environment_satisfaction | gender | hourly_rate | job_involvement | job_level | job_role | job_satisfaction | marital_status | monthly_income | monthly_rate | num_companies_worked | over_time | percent_salary_hike | performance_rating | relationship_satisfaction | stock_option_level | total_working_years | training_times_last_year | work_life_balance | years_at_company | years_in_current_role | years_since_last_promotion | years_with_curr_manager |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| age | 1.0000 | -0.1800 | -0.01800 | 0.00740 | -0.0460 | -0.0100 | 0.2000 | -0.02800 | 0.00910 | -0.03300 | 0.03000 | 0.0230 | 0.50000 | -0.14000 | -0.00350 | 0.0940 | 0.4800 | 0.03200 | 0.2900 | 0.03000 | 0.01200 | 0.00210 | 0.05800 | 0.04300 | 0.6800 | -0.0240 | -0.0310 | 0.3100 | 0.2200 | 0.2200 | 0.20000 |
| attrition | -0.1800 | 1.0000 | 0.12000 | -0.05500 | 0.0480 | 0.0820 | -0.0400 | 0.01100 | -0.07800 | 0.03400 | -0.00110 | -0.1200 | -0.19000 | 0.06000 | -0.09500 | -0.1300 | -0.1800 | 0.01600 | 0.0180 | 0.26000 | -0.02300 | 0.00170 | -0.03300 | -0.12000 | -0.1800 | -0.0470 | -0.0650 | -0.1400 | -0.1600 | -0.0440 | -0.16000 |
| business_travel | -0.0180 | 0.1200 | 1.00000 | -0.01600 | -0.0026 | -0.0097 | -0.0087 | -0.02000 | -0.01100 | -0.04500 | -0.00420 | 0.0290 | -0.01200 | 0.01200 | 0.00870 | -0.0310 | -0.0260 | -0.00840 | -0.0220 | 0.04300 | -0.02600 | 0.00170 | 0.00890 | -0.02800 | 0.0080 | 0.0160 | 0.0042 | 0.0052 | -0.0053 | 0.0052 | -0.00023 |
| daily_rate | 0.0074 | -0.0550 | -0.01600 | 1.00000 | 0.0071 | -0.0050 | -0.0170 | 0.03200 | 0.01800 | -0.01200 | 0.02300 | 0.0460 | 0.00300 | -0.00950 | 0.03100 | 0.0700 | 0.0160 | -0.03200 | 0.0240 | 0.00910 | 0.02300 | 0.00047 | 0.00780 | 0.04200 | 0.0150 | 0.0025 | -0.0380 | -0.0340 | 0.0099 | -0.0330 | -0.02600 |
| department | -0.0460 | 0.0480 | -0.00260 | 0.00710 | 1.0000 | 0.0170 | 0.0080 | 0.00930 | -0.01900 | -0.04200 | -0.00410 | -0.0250 | 0.10000 | 0.66000 | 0.02100 | -0.0560 | 0.0440 | 0.02400 | -0.0430 | 0.00750 | -0.00780 | -0.02500 | -0.02200 | -0.01200 | -0.0160 | 0.0370 | 0.0260 | 0.0230 | 0.0560 | 0.0400 | 0.03400 |
| distance_from_home | -0.0100 | 0.0820 | -0.00970 | -0.00500 | 0.0170 | 1.0000 | 0.0210 | 0.00400 | -0.01600 | -0.00190 | 0.03100 | 0.0088 | 0.00530 | -0.00100 | -0.00370 | 0.0140 | -0.0150 | 0.02700 | -0.0450 | 0.02600 | 0.04000 | 0.02700 | 0.00660 | 0.04500 | 0.0046 | -0.0370 | -0.0270 | 0.0095 | 0.0190 | 0.0100 | 0.01400 |
| education | 0.2000 | -0.0400 | -0.00870 | -0.01700 | 0.0080 | 0.0210 | 1.0000 | -0.02900 | -0.02700 | -0.01700 | 0.01700 | 0.0420 | 0.10000 | 0.00420 | -0.01100 | -0.0041 | 0.0970 | -0.02600 | 0.1300 | -0.02000 | -0.01100 | -0.02500 | -0.00910 | 0.01800 | 0.1500 | -0.0250 | 0.0098 | 0.0690 | 0.0600 | 0.0540 | 0.06900 |
| education_field | -0.0280 | 0.0110 | -0.02000 | 0.03200 | 0.0093 | 0.0040 | -0.0290 | 1.00000 | 0.05100 | 0.00066 | -0.03300 | -0.0039 | -0.03800 | 0.00170 | -0.03100 | -0.0110 | -0.0320 | -0.03400 | 0.0066 | 0.01100 | 0.00130 | 0.00110 | -0.00580 | -0.01900 | -0.0250 | 0.0480 | 0.0440 | -0.0200 | -0.0160 | -0.0056 | -0.00620 |
| environment_satisfaction | 0.0091 | -0.0780 | -0.01100 | 0.01800 | -0.0190 | -0.0160 | -0.0270 | 0.05100 | 1.00000 | 0.00051 | -0.05000 | -0.0083 | 0.00120 | -0.01700 | -0.00680 | 0.0036 | -0.0190 | 0.03800 | 0.0130 | 0.07000 | -0.03200 | -0.03000 | 0.00770 | 0.00340 | -0.0027 | -0.0190 | 0.0280 | 0.0015 | 0.0180 | 0.0160 | -0.00500 |
| gender | -0.0330 | 0.0340 | -0.04500 | -0.01200 | -0.0420 | -0.0019 | -0.0170 | 0.00066 | 0.00051 | 1.00000 | -0.00048 | 0.0180 | -0.03900 | -0.04000 | 0.03300 | 0.0470 | -0.0260 | -0.04100 | -0.0450 | -0.04200 | 0.00270 | -0.01400 | 0.02300 | 0.01300 | -0.0470 | -0.0390 | -0.0028 | -0.0300 | -0.0410 | -0.0270 | -0.03100 |
| hourly_rate | 0.0300 | -0.0011 | -0.00420 | 0.02300 | -0.0041 | 0.0310 | 0.0170 | -0.03300 | -0.05000 | -0.00048 | 1.00000 | 0.0430 | -0.02800 | -0.01900 | -0.07100 | 0.0180 | -0.0150 | -0.01500 | 0.0170 | -0.00780 | -0.00910 | -0.00220 | 0.00130 | 0.05000 | -0.0023 | -0.0085 | -0.0046 | -0.0200 | -0.0240 | -0.0270 | -0.02000 |
| job_involvement | 0.0230 | -0.1200 | 0.02900 | 0.04600 | -0.0250 | 0.0088 | 0.0420 | -0.00390 | -0.00830 | 0.01800 | 0.04300 | 1.0000 | -0.01300 | 0.00660 | -0.02100 | 0.0380 | -0.0180 | -0.01600 | 0.0280 | -0.00350 | -0.01700 | -0.02900 | 0.03400 | 0.02200 | -0.0055 | -0.0150 | -0.0150 | -0.0210 | 0.0087 | -0.0240 | 0.02600 |
| job_level | 0.5000 | -0.1900 | -0.01200 | 0.00300 | 0.1000 | 0.0053 | 0.1000 | -0.03800 | 0.00120 | -0.03900 | -0.02800 | -0.0130 | 1.00000 | -0.08500 | -0.00190 | 0.0770 | 0.9200 | 0.04000 | 0.1400 | 0.00054 | -0.03500 | -0.02100 | 0.02200 | 0.01400 | 0.7800 | -0.0180 | 0.0380 | 0.5300 | 0.3900 | 0.3500 | 0.38000 |
| job_role | -0.1400 | 0.0600 | 0.01200 | -0.00950 | 0.6600 | -0.0010 | 0.0042 | 0.00170 | -0.01700 | -0.04000 | -0.01900 | 0.0066 | -0.08500 | 1.00000 | 0.01800 | -0.0680 | -0.1100 | 0.00530 | -0.0620 | 0.04100 | -0.00085 | -0.02400 | -0.02000 | -0.01900 | -0.1500 | 0.0013 | 0.0280 | -0.0840 | -0.0280 | -0.0460 | -0.04100 |
| job_satisfaction | -0.0035 | -0.0950 | 0.00870 | 0.03100 | 0.0210 | -0.0037 | -0.0110 | -0.03100 | -0.00680 | 0.03300 | -0.07100 | -0.0210 | -0.00190 | 0.01800 | 1.00000 | -0.0240 | -0.0180 | 0.00064 | -0.0660 | 0.02500 | 0.02000 | 0.00230 | -0.01200 | 0.01100 | -0.0200 | -0.0058 | -0.0190 | -0.0038 | -0.0023 | -0.0180 | -0.02800 |
| marital_status | 0.0940 | -0.1300 | -0.03100 | 0.07000 | -0.0560 | 0.0140 | -0.0041 | -0.01100 | 0.00360 | 0.04700 | 0.01800 | 0.0380 | 0.07700 | -0.06800 | -0.02400 | 1.0000 | 0.0750 | -0.02400 | 0.0240 | 0.01800 | -0.01200 | -0.00520 | -0.02300 | 0.66000 | 0.0780 | -0.0110 | -0.0150 | 0.0600 | 0.0660 | 0.0310 | 0.03900 |
| monthly_income | 0.4800 | -0.1800 | -0.02600 | 0.01600 | 0.0440 | -0.0150 | 0.0970 | -0.03200 | -0.01900 | -0.02600 | -0.01500 | -0.0180 | 0.92000 | -0.11000 | -0.01800 | 0.0750 | 1.0000 | 0.03000 | 0.1400 | 0.00650 | -0.02300 | -0.00970 | 0.02300 | 0.00350 | 0.7600 | -0.0180 | 0.0320 | 0.5100 | 0.3700 | 0.3600 | 0.35000 |
| monthly_rate | 0.0320 | 0.0160 | -0.00840 | -0.03200 | 0.0240 | 0.0270 | -0.0260 | -0.03400 | 0.03800 | -0.04100 | -0.01500 | -0.0160 | 0.04000 | 0.00530 | 0.00064 | -0.0240 | 0.0300 | 1.00000 | 0.0100 | 0.02100 | -0.00640 | -0.00980 | -0.00410 | -0.03400 | 0.0260 | 0.0015 | 0.0080 | -0.0240 | -0.0130 | 0.0016 | -0.03700 |
| num_companies_worked | 0.2900 | 0.0180 | -0.02200 | 0.02400 | -0.0430 | -0.0450 | 0.1300 | 0.00660 | 0.01300 | -0.04500 | 0.01700 | 0.0280 | 0.14000 | -0.06200 | -0.06600 | 0.0240 | 0.1400 | 0.01000 | 1.0000 | -0.01700 | -0.00920 | -0.02200 | 0.04700 | 0.01500 | 0.2200 | -0.0650 | -0.0220 | -0.0910 | -0.0770 | -0.0360 | -0.08400 |
| over_time | 0.0300 | 0.2600 | 0.04300 | 0.00910 | 0.0075 | 0.0260 | -0.0200 | 0.01100 | 0.07000 | -0.04200 | -0.00780 | -0.0035 | 0.00054 | 0.04100 | 0.02500 | 0.0180 | 0.0065 | 0.02100 | -0.0170 | 1.00000 | -0.00540 | 0.00440 | 0.04800 | -0.00045 | 0.0130 | -0.0790 | -0.0270 | -0.0120 | -0.0300 | -0.0120 | -0.04200 |
| percent_salary_hike | 0.0120 | -0.0230 | -0.02600 | 0.02300 | -0.0078 | 0.0400 | -0.0110 | 0.00130 | -0.03200 | 0.00270 | -0.00910 | -0.0170 | -0.03500 | -0.00085 | 0.02000 | -0.0120 | -0.0230 | -0.00640 | -0.0092 | -0.00540 | 1.00000 | 0.77000 | -0.04000 | 0.00750 | -0.0210 | -0.0052 | -0.0033 | -0.0360 | -0.0015 | -0.0220 | -0.01200 |
| performance_rating | 0.0021 | 0.0017 | 0.00170 | 0.00047 | -0.0250 | 0.0270 | -0.0250 | 0.00110 | -0.03000 | -0.01400 | -0.00220 | -0.0290 | -0.02100 | -0.02400 | 0.00230 | -0.0052 | -0.0097 | -0.00980 | -0.0220 | 0.00440 | 0.77000 | 1.00000 | -0.03100 | 0.00350 | 0.0067 | -0.0160 | 0.0026 | 0.0034 | 0.0350 | 0.0180 | 0.02300 |
| relationship_satisfaction | 0.0580 | -0.0330 | 0.00890 | 0.00780 | -0.0220 | 0.0066 | -0.0091 | -0.00580 | 0.00770 | 0.02300 | 0.00130 | 0.0340 | 0.02200 | -0.02000 | -0.01200 | -0.0230 | 0.0230 | -0.00410 | 0.0470 | 0.04800 | -0.04000 | -0.03100 | 1.00000 | -0.04600 | 0.0240 | 0.0025 | 0.0200 | 0.0190 | -0.0150 | 0.0330 | -0.00087 |
| stock_option_level | 0.0430 | -0.1200 | -0.02800 | 0.04200 | -0.0120 | 0.0450 | 0.0180 | -0.01900 | 0.00340 | 0.01300 | 0.05000 | 0.0220 | 0.01400 | -0.01900 | 0.01100 | 0.6600 | 0.0035 | -0.03400 | 0.0150 | -0.00045 | 0.00750 | 0.00350 | -0.04600 | 1.00000 | 0.0100 | 0.0110 | 0.0041 | 0.0150 | 0.0510 | 0.0140 | 0.02500 |
| total_working_years | 0.6800 | -0.1800 | 0.00800 | 0.01500 | -0.0160 | 0.0046 | 0.1500 | -0.02500 | -0.00270 | -0.04700 | -0.00230 | -0.0055 | 0.78000 | -0.15000 | -0.02000 | 0.0780 | 0.7600 | 0.02600 | 0.2200 | 0.01300 | -0.02100 | 0.00670 | 0.02400 | 0.01000 | 1.0000 | -0.0360 | 0.0010 | 0.6300 | 0.4600 | 0.4000 | 0.46000 |
| training_times_last_year | -0.0240 | -0.0470 | 0.01600 | 0.00250 | 0.0370 | -0.0370 | -0.0250 | 0.04800 | -0.01900 | -0.03900 | -0.00850 | -0.0150 | -0.01800 | 0.00130 | -0.00580 | -0.0110 | -0.0180 | 0.00150 | -0.0650 | -0.07900 | -0.00520 | -0.01600 | 0.00250 | 0.01100 | -0.0360 | 1.0000 | 0.0280 | 0.0036 | -0.0057 | -0.0021 | -0.00410 |
| work_life_balance | -0.0310 | -0.0650 | 0.00420 | -0.03800 | 0.0260 | -0.0270 | 0.0098 | 0.04400 | 0.02800 | -0.00280 | -0.00460 | -0.0150 | 0.03800 | 0.02800 | -0.01900 | -0.0150 | 0.0320 | 0.00800 | -0.0220 | -0.02700 | -0.00330 | 0.00260 | 0.02000 | 0.00410 | 0.0010 | 0.0280 | 1.0000 | 0.0120 | 0.0500 | 0.0089 | 0.00280 |
| years_at_company | 0.3100 | -0.1400 | 0.00520 | -0.03400 | 0.0230 | 0.0095 | 0.0690 | -0.02000 | 0.00150 | -0.03000 | -0.02000 | -0.0210 | 0.53000 | -0.08400 | -0.00380 | 0.0600 | 0.5100 | -0.02400 | -0.0910 | -0.01200 | -0.03600 | 0.00340 | 0.01900 | 0.01500 | 0.6300 | 0.0036 | 0.0120 | 1.0000 | 0.7600 | 0.6200 | 0.77000 |
| years_in_current_role | 0.2200 | -0.1600 | -0.00530 | 0.00990 | 0.0560 | 0.0190 | 0.0600 | -0.01600 | 0.01800 | -0.04100 | -0.02400 | 0.0087 | 0.39000 | -0.02800 | -0.00230 | 0.0660 | 0.3700 | -0.01300 | -0.0770 | -0.03000 | -0.00150 | 0.03500 | -0.01500 | 0.05100 | 0.4600 | -0.0057 | 0.0500 | 0.7600 | 1.0000 | 0.5500 | 0.71000 |
| years_since_last_promotion | 0.2200 | -0.0440 | 0.00520 | -0.03300 | 0.0400 | 0.0100 | 0.0540 | -0.00560 | 0.01600 | -0.02700 | -0.02700 | -0.0240 | 0.35000 | -0.04600 | -0.01800 | 0.0310 | 0.3600 | 0.00160 | -0.0360 | -0.01200 | -0.02200 | 0.01800 | 0.03300 | 0.01400 | 0.4000 | -0.0021 | 0.0089 | 0.6200 | 0.5500 | 1.0000 | 0.51000 |
| years_with_curr_manager | 0.2000 | -0.1600 | -0.00023 | -0.02600 | 0.0340 | 0.0140 | 0.0690 | -0.00620 | -0.00500 | -0.03100 | -0.02000 | 0.0260 | 0.38000 | -0.04100 | -0.02800 | 0.0390 | 0.3500 | -0.03700 | -0.0840 | -0.04200 | -0.01200 | 0.02300 | -0.00087 | 0.02500 | 0.4600 | -0.0041 | 0.0028 | 0.7700 | 0.7100 | 0.5100 | 1.00000 |
Otrzymane powyżej wyniki prezentuje poniższa macierz korealacji.
corrplot(
macierz_kor_numeric_mx,
method = "color",
type = "upper",
col = colorRampPalette(c("#4477AA", "white", "#BB4444"))(200),
insig = "blank",
tl.cex = 0.9 )
6. Wnioskowanie statystyczne
6.1. Odejścia z firmy a miesięczny dochód
W kontekście analizy odejść z firmy kluczowe jest określenie, czy wynagrodzenie stanowi czynnik różnicujący pracowników, którzy pozostali w organizacji, i tych, którzy zdecydowali się na odejście. W tym celu formułujemy następujące hipotezy:
- Hipoteza zerowa (H₀): Nie ma statystycznie istotnej różnicy w wysokości miesięcznego dochodu między pracownikami, którzy pozostali w firmie, a tymi, którzy ją opuścili.
- Hipoteza alternatywna (H₁): Istnieje statystycznie istotna różnica w wysokości miesięcznego dochodu między pracownikami, którzy pozostali w firmie, a tymi, którzy ją opuścili.
ggbetweenstats(data = HR_final, x = attrition, y = monthly_income) +
scale_color_manual(values = c("#219ebc", "#fb8500"))
W przeprowadzonej analizie porównano dochód miesięczny (monthly_income) między pracownikami, którzy opuścili firmę (attrition = “Tak”), a tymi, którzy pozostali (attrition = “Nie”). Zastosowano test t-Studenta Welch’a, który wyniósł t(400,74) = 8,29. Wartość p = 1,70e-15, co wskazuje na istotną statystycznie różnicę w dochodach między osobami, które nie odeszły i odeszły, tym samym odrzucamy hipotezę zerową.
Średni dochód w grupie “Nie” wynosił 6909,16 USD, a w grupie “Tak” 4685,64 USD, co oznacza, że osoby, które opuściły firmę, zarabiały średnio o około 20% mniej. Wielkość efektu, mierzona za pomocą współczynnika Hedges’a g = 0,53, wskazuje na średni efekt tej różnicy.
Wizualizacja wyników na wykresie wiolinowym pokazuje, że w grupie “Nie” rozkład dochodów jest gęstszy i rozciąga się na wyższe wartości, podczas gdy w grupie “Tak” dochody koncentrują się głównie wokół niższych i średnich wartości, co sugeruje, że firmę częściej opuszczają osoby z niższymi dochodami.
Podsumowując, test t wykazał istotną różnicę w dochodach miesięcznych, z osobami o wyższych dochodach bardziej skłonnymi do pozostania w organizacji. Wielkość efektu sugeruje średnią siłę tej zależności.
6.2. Satysfakcja z pracy a dział przedsiębiorstwa
Badanie zależności między działem a satysfakcją z pracy jest kluczowe dla zarządzania zasobami ludzkimi i rozwoju organizacji, ponieważ może ujawnić obszary wymagające interwencji. Niski poziom satysfakcji w niektórych działach może wskazywać na problemy takie jak złe zarządzanie, niewłaściwa komunikacja, stres czy nieodpowiednia kultura organizacyjna, co wymaga poprawy warunków pracy poprzez szkolenia menedżerskie czy zmiany organizacyjne. Ponadto, analiza może pomóc w optymalizacji procesów rekrutacyjnych, kierując pracowników do działów z wyższą satysfakcją, co pozytywnie wpłynie na ich zaangażowanie. W przypadku wykrycia niższej satysfakcji w określonych działach, firma może wdrożyć strategię retencyjną, aby zminimalizować rotację i zmniejszyć koszty rekrutacji oraz szkoleń nowych pracowników. W celu weryfikacji tej zależności formułujemy następujące hipotezy:
- Hipoteza zerowa (H₀): Satysfakcja z pracy nie różni się w zależności od działu w firmie
- Hipoteza alternatywna (H₁): Satysfakcja z pracy różni się w zależności od działu w firmie.
hrfinal <- HR_final %>% mutate(job_satisfaction = as.numeric(job_satisfaction), department = as.numeric(department))
ggbetweenstats(data = hrfinal, x = department, y = job_satisfaction)
W przeprowadzonej analizie badano zależność między działem w firmie a satysfakcją z pracy. Zastosowano test F Welch’a (F(2,167,47) = 0,52), który nie wykazał istotnej statystycznie różnicy w średniej satysfakcji z pracy pomiędzy działami. Wartość p = 0,59 jest większa od poziomu 0,05, co sugeruje, że brak jest wystarczających dowodów, by stwierdzić, iż średnia satysfakcja z pracy różni się istotnie pomiędzy działami.
Średnia satysfakcja z pracy w dziale HR wynosiła 2,60, co wskazuje na stosunkowo niższe wartości w porównaniu z innymi działami. W dziale Badań i Rozwoju średnia wyniosła 2,73, a w dziale sprzedaży 2,75.
6.3. Lata pracy w przedsiębiorstwie a miesięczny dochód
Analiza zależności między liczbą lat przepracowanych w firmie a miesięcznym dochodem ma istotne znaczenie dla oceny polityki wynagrodzeń oraz procesów motywacyjnych w organizacji. Zrozumienie tej zależności może pomóc firmie ocenić, czy wynagrodzenia są adekwatne do doświadczenia pracowników oraz czy firma oferuje odpowiednie zachęty finansowe dla osób, które pozostają w organizacji przez długi czas.
Wyniki analizy mogą również pomóc w ustaleniu, czy obecna struktura wynagrodzeń motywuje pracowników do długoterminowego pozostania w firmie, czy też powoduje stagnację. Jeśli istnieje silna korelacja między stażem a wynagrodzeniem, może to sugerować, że firma stawia na nagradzanie lojalności. Z drugiej strony, brak takiej korelacji może wskazywać, że inne czynniki, takie jak wyniki indywidualne, negocjacje płacowe lub zmiany w organizacji, mają większy wpływ na wysokość pensji.
Ponadto, analiza ta może stanowić punkt wyjścia do dalszych badań na temat struktur wynagrodzeń, awansów oraz polityk motywacyjnych w firmie, które mogą być dostosowane do różnych poziomów doświadczenia pracowników.
- Hipoteza zerowa (H₀): Liczba lat przepracowanych w firmie nie ma wpływu na miesięczny dochód.
- Hipoteza alternatywna (H₁): Liczba lat przepracowanych w firmie ma wpływ na miesięczny dochód.
W przeprowadzonej analizie badano zależność między liczbą lat pracy w firmie (years_at_company) a dochodem miesięcznym (monthly_income). Zastosowano test t-Studenta, który wykazał istotną statystycznie korelację (t = 22,65, p = 1,10e-97), co sugeruje, że istnieje silna zależność między tymi dwiema zmiennymi. Uzyskana wartość p < 0,05 wskazuje na istotność tego efektu. Współczynnik korelacji Pearsona wyniósł r = 0,51, co wskazuje na umiarkowaną, dodatnią korelację między liczbą lat pracy a dochodem miesięcznym. Oznacza to, że wraz ze wzrostem liczby lat pracy w firmie, dochody miesięczne mają tendencję do wzrostu, ale z umiarkowaną siłą.
Na wykresie scatterplot widoczna jest koncentracja punktów w lewym dolnym rogu, co oznacza dużą liczbę osób z krótkim stażem i niskimi dochodami. Widzimy również osoby z wyższymi dochodami, ale krótkim stażem, szczególnie w górnej części wykresu. Z kolei brak jest osób z niskimi dochodami przy długim stażu pracy, co sugeruje, że długoletni pracownicy rzadko mają niskie wynagrodzenie.
Podsumowując, wyniki wskazują na umiarkowaną zależność między stażem a dochodami, z tendencją do wyższych wynagrodzeń w przypadku dłuższego stażu, ale także niskimi dochodami w przypadku stażu do 10 lat.
6.4. Delegacje a odejścia z firmy
Analiza wpływu podróży służbowych na odejścia z firmy może dostarczyć istotnych informacji o tym, jak organizacja zarządza pracownikami, którzy regularnie podróżują służbowo. Jeśli wykryjemy istotną różnicę w liczbie odejść między pracownikami podróżującymi często a tymi, którzy nie podróżują wcale, firma może podjąć działania mające na celu poprawę warunków pracy, np. poprzez wprowadzenie elastyczniejszych godzin pracy, lepszą równowagę między życiem prywatnym a zawodowym, czy dodatkowe benefity dla osób podróżujących.
- Hipoteza zerowa (H₀): Rodzaj podróży służbowych nie ma wpływu na decyzję o odejściu z firmy.
- Hipoteza alternatywna (H₁):Rodzaj podróży służbowych ma wpływ na decyzję o odejściu z firmy.
ggpiestats(data = HR_final, x = business_travel, y = attrition) +
scale_fill_manual(values = c("#669bbc", "#c1121f", "#fdf0d5")) +
theme_minimal()
W przeprowadzonej analizie badano zależność między częstotliwością delegacji (BusinessTravel) a odstąpieniem od pracy (Attrition). Test Chi-kwadrat Pearsona (χ² = 20,75, df = 2, p = 3,11e-05) wykazał istotną statystycznie zależność między tymi zmiennymi, co sugeruje, że częstotliwość delegacji ma związek z decyzją o odejściu z firmy. Wynik p < 0,05 potwierdza istotność tej zależności.
Przedział ufności 95% (CI = [0,05; 0,16]) wskazuje, że siła tej zależności jest umiarkowana, a wartość współczynnika Cramera wskazuje na średnią wielkość efektu.
Na wykresie kołowym dla osób, które pozostały w firmie (Attrition = “Nie”), widoczna jest dominacja osób podróżujących rzadko (72%), z 17% osób podróżujących często i 11% niepodróżujących. Z kolei w grupie osób, które odeszły z firmy (Attrition = “Tak”), widać, że większość pracowników podróżuje rzadko (65%), ale w tej grupie jest wyższy odsetek osób podróżujących często (29%), a tylko 6% nie podróżuje w ogóle.
Wyniki sugerują, że osoby, które rzadziej podróżują służbowo, mają tendencję do pozostawania w firmie. Jednak w grupie osób, które odeszły, znaczny odsetek podróżujących często wskazuje, że intensywność delegacji może wpływać na decyzję o rotacji, być może wskazując na negatywne skutki częstych wyjazdów służbowych dla lojalności wobec firmy.
7. Podsumowanie
W niniejszym raporcie przeprowadzono kompleksowy proces przygotowania i analizy danych, który rozpoczęto od wstępnej analizy, pozwalającej na identyfikację problemów związanych z jakością zbioru danych. Na tym etapie dokonano czyszczenia danych oraz imputacji brakujących wartości, wykorzystując metody oparte na korelacjach oraz charakterze braków danych. Następnie oczyszczonym danym nadano odpowiednie kategorie, co umożliwiło ich dalszą analizę wizualną.
Kolejnym etapem badania była wizualizacja danych, która pozwoliła na przedstawienie zależności między poszczególnymi zmiennymi w sposób czytelny dla odbiorcy. Wykonanie analizy opisowej umożliwiło wykrycie potencjalnych zależności i wzorców występujących w zbiorze danych, co stanowiło podstawę do dalszych analiz.
Wyniki wnioskowania statystycznego wskazały, że na decyzję o odejściu pracowników z przedsiębiorstwa istotny wpływ mają zmienne związane z częstotliwością podróży służbowych (business_travel), satysfakcji z pracy (job_satisfaction) oraz miesięcznym dochodem (monthly_income). Oznacza to, że miejsce odbywania podróży służbowych, dochody oraz satysfakcja z pracy stanowią kluczowe determinanty decyzji o odejściu pracowników. Podsumowując, przeprowadzone analizy pozwoliły na wyodrębnienie najważniejszych czynników wpływających na odejście pracowników z przedsiębiorstwa, co stanowi cenny wkład w dalsze badania nad dynamiką zatrudnienia.