diff --git a/bambi/priors/scaler.py b/bambi/priors/scaler.py index 9e88a64d5..d6ffc716c 100644 --- a/bambi/priors/scaler.py +++ b/bambi/priors/scaler.py @@ -114,7 +114,7 @@ def scale_threshold(self): threshold = self.model.components["threshold"] if isinstance(threshold, ConstantComponent) and threshold.prior.auto_scale: response_level_n = len(np.unique(self.response_component.response_term.data)) - mu = np.round(np.linspace(-2, 2, num=response_level_n - 1), 2) + mu = np.zeros(response_level_n - 1) threshold.prior = Prior("Normal", mu=mu, sigma=1) def scale(self): diff --git a/docs/notebooks/data/hr_employee_attrition.tsv.txt b/docs/notebooks/data/hr_employee_attrition.tsv.txt new file mode 100644 index 000000000..28ee7a020 --- /dev/null +++ b/docs/notebooks/data/hr_employee_attrition.tsv.txt @@ -0,0 +1,1471 @@ +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 +41 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 5130 24907 1 Y No 23 4 4 80 1 10 3 3 10 7 1 7 +37 Yes 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 2909 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 No 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 3407 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 No 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 +43 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 2014 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 5381 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 No 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 +38 No 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 +55 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 +36 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 No 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 6220 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 13245 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 5126 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 2859 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 7260 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 No 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 No 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 5163 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 No 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 Yes 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 No 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 Yes Travel_Frequently 658 Research & Development 7 3 Life Sciences 1 147 1 Male 66 1 2 Laboratory Technician 3 Single 6074 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 2774 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 7428 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 16959 19494 1 Y Yes 12 3 4 80 2 25 3 4 25 12 4 12 +30 No 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 +22 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 No 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 +41 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 5902 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 +38 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 2720 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 2238 6961 2 Y No 21 4 4 80 1 7 2 3 5 0 1 4 +19 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 5605 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 +32 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 No 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 +36 No Travel_Frequently 1480 Research & Development 3 2 Medical 1 238 4 Male 30 3 1 Laboratory Technician 2 Single 2088 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 +31 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 No 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 No 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 19033 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 19999 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 No 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 +42 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 2741 16523 8 Y Yes 15 3 3 80 1 15 2 4 7 2 3 7 +38 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 2328 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 4876 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 9396 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 +30 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 +29 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 No 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 19517 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 16064 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 3931 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 +32 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 2911 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 +51 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 No 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 6397 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 17584 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 18665 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 +31 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 Yes 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 13525 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 5482 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 1200 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 +38 No Travel_Rarely 849 Research & Development 25 2 Life Sciences 1 421 1 Female 81 2 3 Research Director 2 Married 12061 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 5660 17056 2 Y Yes 13 3 4 80 1 12 2 3 5 3 1 2 +31 No 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 +31 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 No 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 Yes 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 +27 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 2119 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 +30 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 No 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 No 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 No 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 +21 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 No 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 No 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 No 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 +34 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 +37 No 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 No Travel_Rarely 1305 Research & Development 10 4 Life Sciences 1 518 3 Male 49 3 2 Manufacturing Director 2 Single 4197 21123 2 Y Yes 12 3 4 80 0 18 2 2 1 0 0 1 +43 No 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 13212 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 +38 No Travel_Frequently 1490 Research & Development 2 2 Life Sciences 1 556 4 Male 42 3 1 Laboratory Technician 4 Married 1702 12106 1 Y Yes 23 4 3 80 1 1 3 3 1 0 0 0 +40 No 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 No 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 +19 Yes 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 No 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 +34 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 No 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 No 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 +33 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 No 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 6811 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 No 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 No 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 2400 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 +49 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 +24 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 5460 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 No 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 2836 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 +43 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 6322 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 1009 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 2819 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 No Travel_Frequently 1410 Sales 3 1 Medical 1 714 4 Female 71 4 2 Sales Executive 4 Divorced 4647 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 Yes 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 +39 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 +50 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 +38 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 No 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 +28 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 3708 2104 2 Y No 14 3 3 80 0 9 5 3 5 2 1 4 +47 No 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 No 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 11103 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 No 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 5309 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 5121 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 Yes 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 Yes 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 No 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 No 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 +34 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 No 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 +33 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 +47 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 2506 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 6062 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 4382 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 +32 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 +30 No 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 4434 11806 1 Y No 13 3 4 80 1 10 3 2 9 8 7 8 +38 No Travel_Frequently 471 Research & Development 12 3 Life Sciences 1 837 1 Male 45 2 2 Healthcare Representative 1 Divorced 6288 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 No 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 +51 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 No 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 +52 No Travel_Frequently 890 Research & Development 25 4 Medical 1 867 3 Female 81 2 4 Manufacturing Director 4 Married 13826 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 6334 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 +22 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 4194 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 +24 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 No 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 Yes 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 No 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 14275 20206 6 Y No 18 3 3 80 0 33 0 3 12 9 3 8 +43 No 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 2795 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 No Travel_Rarely 661 Research & Development 9 2 Life Sciences 1 913 2 Male 61 3 1 Research Scientist 1 Married 2559 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 +43 No Travel_Rarely 177 Research & Development 8 3 Life Sciences 1 920 1 Female 55 3 2 Manufacturing Director 2 Divorced 4765 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 +21 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 +41 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 2008 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 6244 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 No 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 No 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 4678 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 2413 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 +26 No Travel_Frequently 1283 Sales 1 3 Medical 1 956 3 Male 52 2 2 Sales Executive 1 Single 4294 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 No 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 3617 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 10325 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 2479 26227 4 Y No 24 4 1 80 0 7 4 3 1 0 0 0 +53 No Travel_Rarely 1376 Sales 2 2 Medical 1 981 3 Male 45 3 4 Manager 3 Divorced 14852 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 5666 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 No 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 10845 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 4240 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 No 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 Yes 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 6861 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 4969 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 No 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 2342 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 No 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 Yes Travel_Frequently 342 Research & Development 2 3 Life Sciences 1 1053 1 Male 57 3 1 Research Scientist 1 Married 2042 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 1052 23384 1 Y No 22 4 2 80 0 1 5 3 1 0 0 0 +38 No 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 4107 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 +36 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 No 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 9957 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 8823 24608 0 Y No 18 3 1 80 1 20 4 2 19 9 1 9 +40 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 +34 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 No 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 +52 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 7351 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 5056 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 Yes 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 4766 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 +34 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 Yes 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 5343 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 +19 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 +44 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 2290 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 No 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 No Travel_Frequently 1146 Sales 25 3 Medical 1 1220 2 Female 82 3 2 Sales Executive 3 Married 4539 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 +32 No 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 2781 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 +36 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 No 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 +48 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 No 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 1118 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 18789 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 No 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 10231 20364 3 Y No 14 3 4 80 0 23 3 4 21 7 15 17 +36 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 Yes 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 No 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 No 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 +39 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 9094 17235 2 Y Yes 12 3 3 80 0 9 2 3 5 4 1 0 +52 Yes 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 +30 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 9852 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 No 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 No 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 No 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 No Travel_Frequently 607 Sales 7 3 Marketing 1 1362 1 Female 83 4 2 Sales Executive 1 Married 4639 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 No 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 6377 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 4724 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 +51 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 2996 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 4078 20497 0 Y Yes 13 3 1 80 3 4 3 2 3 2 1 2 +39 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 +25 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 No 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 2394 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 +25 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 Yes 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 No 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 +56 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 No 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 3646 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 No 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 No 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 No 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 +28 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 3172 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 2033 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 +30 No Travel_Rarely 330 Human Resources 1 3 Life Sciences 1 1499 3 Male 46 3 1 Human Resources 3 Divorced 2064 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 No 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 No 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 +33 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 No 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 16032 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 14411 24450 1 Y Yes 13 3 4 80 0 32 2 3 32 6 13 9 +34 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 No Travel_Rarely 1210 Research & Development 2 3 Medical 1 1542 3 Male 68 2 1 Laboratory Technician 2 Married 4841 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 +42 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 No 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 No 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 No Travel_Rarely 1402 Sales 28 4 Life Sciences 1 1554 2 Female 98 2 1 Sales Representative 3 Married 2430 26204 0 Y No 23 4 1 80 2 6 5 3 5 3 4 2 +32 No 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 6392 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 Yes 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 +32 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 3407 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 +40 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 No 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 2690 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 +22 No 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 +32 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 +45 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 +36 No Travel_Rarely 559 Research & Development 12 4 Life Sciences 1 1614 3 Female 76 3 2 Manufacturing Director 3 Married 4663 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 3211 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 5377 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 4877 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 1569 18420 1 Y Yes 12 3 3 80 0 0 2 4 0 0 0 0 +47 No 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 No 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 10435 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 +37 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 5770 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 No 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 5869 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 16413 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 2783 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 2013 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 No 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 4187 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 No 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 10883 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 No 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 7879 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 +41 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 4502 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 +45 No 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 +46 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 Yes 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 2180 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 No 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 8564 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 No Travel_Rarely 305 Research & Development 16 3 Life Sciences 1 1763 3 Male 58 4 2 Healthcare Representative 3 Married 5294 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 +42 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 No 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 No 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 4001 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 Yes 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 No 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 No 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 Yes 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 5762 24442 2 Y No 14 3 3 80 1 15 6 3 7 7 1 7 +38 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 +41 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 +46 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 No 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 +54 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 +26 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 No 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 2956 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 No 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 No 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 No 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 4774 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 No 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 3886 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 2933 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 No Travel_Frequently 117 Research & Development 22 3 Medical 1 1900 4 Female 58 3 4 Manager 4 Divorced 17174 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 No 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 8103 16495 3 Y Yes 12 3 3 80 0 9 3 2 4 2 0 1 +31 No 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 +33 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 No 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 5747 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 No 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 19161 13738 3 Y No 15 3 4 80 0 28 3 3 5 4 4 3 +42 No 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 +34 No 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 +26 No Travel_Rarely 157 Research & Development 1 3 Medical 1 1952 3 Male 95 3 1 Laboratory Technician 1 Single 2867 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 6667 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 +53 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 No 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 No 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 No 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 5677 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 +25 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 No 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 11957 17231 0 Y No 18 3 1 80 2 14 3 1 13 8 5 12 +35 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 4878 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 +38 No 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 +23 Yes 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 Yes 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 +36 No 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 8938 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 No 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 No 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 4025 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 \ No newline at end of file diff --git a/docs/notebooks/gallery.yml b/docs/notebooks/gallery.yml index 3d4c5a36b..c9864ce68 100644 --- a/docs/notebooks/gallery.yml +++ b/docs/notebooks/gallery.yml @@ -88,6 +88,18 @@ subtitle: When the outcome is mostly zeros and or is overdispersed href: zero_inflated_regression.ipynb thumbnail: thumbnails/zero_inflated_pps.png + - title: Ordinal regression + subtitle: Model ordered category outcomes + href: ordinal_regression.ipynb + thumbnail: thumbnails/ordinal_regression.png + - title: Zero inflated models + subtitle: When the outcome is mostly zeros and or is overdispersed + href: zero_inflated_regression.ipynb + thumbnail: thumbnails/zero_inflated_pps.png + - title: Ordinal regression + subtitle: Model ordered category outcomes + href: ordinal_regression.ipynb + thumbnail: thumbnails/ordinal_regression.png - category: More advanced models description: "" tiles: diff --git a/docs/notebooks/ordinal_regression.ipynb b/docs/notebooks/ordinal_regression.ipynb new file mode 100644 index 000000000..65db2c2fc --- /dev/null +++ b/docs/notebooks/ordinal_regression.ipynb @@ -0,0 +1,1839 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "import numpy as np\n", + "import pandas as pd\n", + "import warnings\n", + "\n", + "import bambi as bmb\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "warnings.filterwarnings(\"ignore\", category=FutureWarning)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ordinal Regression\n", + "\n", + "In some scenarios, the response variable is discrete, like a count, and ordered. Common examples of such data come from questionnaires where the respondent is asked to rate a product, service, or experience on a scale. This scale is often referred to as a [Likert scale](https://en.wikipedia.org/wiki/Likert_scale). For example, a five-level Likert scale could be:\n", + "\n", + "- 1 = Strongly disagree\n", + "- 2 = Disagree\n", + "- 3 = Neither agree nor disagree\n", + "- 4 = Agree\n", + "- 5 = Strongly agree\n", + "\n", + "The result is a set of **ordered categories** where each category has an associated numeric value (1-5). However, you can't compute a meaningful difference between the categories. Moreover, the response variable can also be a count where meaningful differences can be computed. For example, a restaurant can be rated on a scale of 1-5 stars where 1 is the worst and 5 is the best. Yes, you can compute the difference between 1 and 2 stars, but it is often treated as ordinal in an applied setting.\n", + "\n", + "Ordinal data presents three challenges when modelling:\n", + "\n", + "1. Unlike a count, the differences in the values are not necessarily equidistant or meaningful. For example, computing the difference between \"Strongly disagree\" and \"Disagree\". Or, in the case of the restaurant rating, it may be much harder for a restuarant to go from 4 to 5 stars than from 2 to 3 stars. \n", + "2. The distribution of ordinal responses may be nonnormal as the response is not continuous; particularly if larger response levels are infrequently chosen compared to lower ones.\n", + "3. The variances of the unobserved variables that underlie the observed ordered category may differ between the category, time points, etc. \n", + "\n", + "Thus, treating ordered categories as continuous is not appropriate. To this extent, Bambi supports two classes of ordinal regression models: (1) cumulative, and (2) sequential. Below, it is demonstrated how to fit these two models using Bambi to overcome the challenges of ordered category response data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cumulative model\n", + "\n", + "A cumulative model assumes that the observed ordinal variable $Y$ originates from the \"categorization\" of a latent continuous variable $Z$. To model the categorization process, the model assumes that there are $K$ thresholds (or cutpoints) $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories of $Y$. The subscript $k$ in $\\tau_k$ is an index that associates that threshold to a particular category $k$. For example, if the response has three categories such as \"disagree\", \"neither agree nor disagree\", and \"agree\", then there are two thresholds $\\tau_1$ and $\\tau_2$ that partition $Z$ into $K+1 = 3$ categories. Additionally, if we assume $Z$ to have a certain distribution (e.g., Normal) with a cumulative distribution function $F$, the probability of $Y$ being equal to category $k$ is\n", + "\n", + "$$P(Y = k) = F(\\tau_k) - F(\\tau_{k-1})$$\n", + "\n", + "where $F(\\tau)$ is a cumulative probability. For example, suppose we are interested in the probability of each category stated above, and have two thresholds $\\tau_1 = -1, \\tau_2 = 1$ for the three categories. Additionally, if we assume $Z$ to be normally distributed with $\\sigma = 1$ and a cumulative distribution function $\\Phi$ then\n", + "\n", + "$$P(Y = 1) = \\Phi(\\tau_1) = \\Phi(-1)$$\n", + "\n", + "$$P(Y = 2) = \\Phi(\\tau_2) - \\Phi(\\tau_1) = \\Phi(1) - \\Phi(-1)$$\n", + "\n", + "$$P(Y = 3) = 1 - \\Phi(\\tau_2) = 1 - \\Phi(1)$$\n", + "\n", + "\n", + "But how to set the values of the thresholds? By default, Bambi uses a Normal distribution with a grid of evenly spaced $\\mu$ that depends on the number of response levels as the prior for the thresholds. Additionally, since the thresholds need to be orderd, Bambi applies a transformation to the values such that the order is preserved. Furthermore, the model specification for ordinal regression typically transforms the cumulative probabilities using the log-cumulative-odds (logit) transformation. Therefore, the learned parameters for the thresholds $\\tau$ will be logits.\n", + "\n", + "Lastly, as each $F(\\tau)$ implies a cumulative probability for each category, the largest response level always has a cumulative probability of 1. Thus, we effectively do not need a parameter for it due to the law of total probability. For example, for three response values, we only need two thresholds as two thresholds partition $Z$ into $K+1$ categories." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### The moral intuition dataset\n", + "\n", + "To illustrate an cumulative ordinal model, we will model data from a series of experiments conducted by philsophers (this example comes from Richard McElreath's [Statistical Rethinking](https://xcelab.net/rm/statistical-rethinking/)). The experiments aim to collect empirical evidence relevant to debates about moral intuition, the forms of reasoning through which people develop judgments about the moral goodness and badness of actions. \n", + "\n", + "In the dataset there are 12 columns and 9930 rows, comprising data for 331 unique individuals. The response we are interested in `response`, is an integer from 1 to 7 indicating how morally permissible the participant found the action to be taken (or not) in the story. The predictors are as follows:\n", + "\n", + "- `action`: a factor with levels 0 and 1 where 1 indicates that the story contained \"harm caused by action is morally worse than equivalent harm caused by omission\".\n", + "- `intention`: a factor with levels 0 and 1 where 1 indicates that the story contained \"harm intended as the means to a goal is morally worse than equivalent harm foreseen as the side effect of a goal\".\n", + "- `contact`: a factor with levels 0 and 1 where 1 indicates that the story contained \"using physical contact to cause harm to a victim is morally worse than causing equivalent harm to a victim without using physical contact\"." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "trolly = pd.read_csv(\"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/Trolley.csv\", sep=\";\")\n", + "trolly = trolly[[\"response\", \"action\", \"intention\", \"contact\"]]\n", + "trolly[\"action\"] = pd.Categorical(trolly[\"action\"], ordered=False)\n", + "trolly[\"intention\"] = pd.Categorical(trolly[\"intention\"], ordered=False)\n", + "trolly[\"contact\"] = pd.Categorical(trolly[\"contact\"], ordered=False)\n", + "trolly[\"response\"] = pd.Categorical(trolly[\"response\"], ordered=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[4, 3, 5, 2, 1, 7, 6]\n", + "Categories (7, int64): [1 < 2 < 3 < 4 < 5 < 6 < 7]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 7 ordered categories from 1-7\n", + "trolly.response.unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Intercept only model\n", + "\n", + "Before we fit a model with predictors, let's attempt to recover the parameters of an ordinal model using only the thresholds to get a feel for the cumulative family. Traditionally, in Bambi if we wanted to recover the parameters of the likelihood, we would use an intercept only model and write the formula as `response ~ 1` where `1` indicates to include the intercept. However, in the case of ordinal regression, the thresholds \"take the place\" of the intercept. Thus, we can write the formula as `response ~ 0` to indicate that we do not want to include an intercept. To fit a cumulative ordinal model, we pass `family=\"cumulative\"`. To compare the thresholds only model, we compute the empirical log-cumulative-odds of the categories directly from the data below and generate a bar plot of the response probabilities." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/rl/y69t95y51g90tvd6gjzzs59h0000gn/T/ipykernel_22293/1548491577.py:3: RuntimeWarning: invalid value encountered in log\n", + " logit_func = lambda x: np.log(x / (1 - x))\n" + ] + }, + { + "data": { + "text/plain": [ + "array([-1.91609116, -1.26660559, -0.718634 , 0.24778573, 0.88986365,\n", + " 1.76938091, nan])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pr_k = trolly.response.value_counts().sort_index().values / trolly.shape[0]\n", + "cum_pr_k = np.cumsum(pr_k)\n", + "logit_func = lambda x: np.log(x / (1 - x))\n", + "cum_logit = logit_func(cum_pr_k)\n", + "cum_logit" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(7, 3))\n", + "plt.bar(np.arange(1, 8), pr_k)\n", + "plt.ylabel(\"Probability\")\n", + "plt.xlabel(\"Response\")\n", + "plt.title(\"Empirical probability of each response category\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = bmb.Model(\"response ~ 0\", data=trolly, family=\"cumulative\")\n", + "idata = model.fit(random_seed=1234)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, the components of the model are outputed. Notice how the thresholds are a grid of six values ranging from -2 to 2. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: response ~ 0 + action + intention + contact + action:intention + contact:intention\n", + " Family: cumulative\n", + " Link: p = logit\n", + " Observations: 9930\n", + " Priors: \n", + " target = p\n", + " Common-level effects\n", + " action ~ Normal(mu: 0.0, sigma: 5.045)\n", + " intention ~ Normal(mu: 0.0, sigma: 5.0111)\n", + " contact ~ Normal(mu: 0.0, sigma: 6.25)\n", + " action:intention ~ Normal(mu: 0.0, sigma: 6.7082)\n", + " contact:intention ~ Normal(mu: 0.0, sigma: 8.3333)\n", + " \n", + " Auxiliary parameters\n", + " threshold ~ Normal(mu: [-2. -1.2 -0.4 0.4 1.2 2. ], sigma: 1.0, transform: ordered)\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
response_threshold[0]-1.9170.030-1.974-1.8630.00.04097.03163.01.0
response_threshold[1]-1.2670.024-1.312-1.2200.00.05391.03302.01.0
response_threshold[2]-0.7190.021-0.760-0.6810.00.05439.03698.01.0
response_threshold[3]0.2480.0200.2110.2870.00.05416.03644.01.0
response_threshold[4]0.8900.0220.8470.9300.00.04966.03439.01.0
response_threshold[5]1.7700.0271.7211.8230.00.04785.03368.01.0
\n", + "
" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", + "response_threshold[0] -1.917 0.030 -1.974 -1.863 0.0 0.0 \n", + "response_threshold[1] -1.267 0.024 -1.312 -1.220 0.0 0.0 \n", + "response_threshold[2] -0.719 0.021 -0.760 -0.681 0.0 0.0 \n", + "response_threshold[3] 0.248 0.020 0.211 0.287 0.0 0.0 \n", + "response_threshold[4] 0.890 0.022 0.847 0.930 0.0 0.0 \n", + "response_threshold[5] 1.770 0.027 1.721 1.823 0.0 0.0 \n", + "\n", + " ess_bulk ess_tail r_hat \n", + "response_threshold[0] 4097.0 3163.0 1.0 \n", + "response_threshold[1] 5391.0 3302.0 1.0 \n", + "response_threshold[2] 5439.0 3698.0 1.0 \n", + "response_threshold[3] 5416.0 3644.0 1.0 \n", + "response_threshold[4] 4966.0 3439.0 1.0 \n", + "response_threshold[5] 4785.0 3368.0 1.0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(idata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Viewing the summary dataframe, we see a total of six `response_threshold` coefficients. Why six? Remember, we get the last parameter for free. Since there are seven categories, we only need six cutpoints. The index (using zero based indexing) of the `response_threshold` indicates the category that the threshold is associated with. Comparing to the empirical log-cumulative-odds computation above, the mean of the posterior distribution for each category is close to the empirical value.\n", + "\n", + "As the the log cumalative link is used, we need to apply the inverse of the logit function to transform back to cumulative probabilities. Below, we plot the cumulative probabilities for each category. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "expit_func = lambda x: 1 / (1 + np.exp(-x))\n", + "cumprobs = expit_func(idata.posterior.response_threshold).mean((\"chain\", \"draw\"))\n", + "cumprobs = np.append(cumprobs, 1)\n", + "\n", + "plt.figure(figsize=(7, 3))\n", + "plt.plot(sorted(trolly.response.unique()), cumprobs, marker='o')\n", + "plt.ylabel(\"Cumulative probability\")\n", + "plt.xlabel(\"Response category\")\n", + "plt.title(\"Cumulative probabilities of response categories\");" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3))\n", + "for i in range(6):\n", + " outcome = expit_func(idata.posterior.response_threshold).sel(response_threshold_dim=i).to_numpy().flatten()\n", + " ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n", + "ax.set_xlabel(\"Probability\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.set_title(\"Cumulative Probability by Category\")\n", + "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can take the derivative of the cumulative probabilities to get the posterior probabilities for each category. Notice how the posterior probabilities in the barplot below are close to the empirical probabilities in barplot above." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# derivative\n", + "ddx = np.diff(cumprobs)\n", + "probs = np.insert(ddx, 0, cumprobs[0])\n", + "\n", + "plt.figure(figsize=(7, 3))\n", + "plt.bar(sorted(trolly.response.unique()), probs)\n", + "plt.ylabel(\"Probability\")\n", + "plt.xlabel(\"Response category\")\n", + "plt.title(\"Posterior Probability of each response category\");" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3))\n", + "for i in range(6):\n", + " outcome = expit_func(idata.posterior.response_threshold).sel(response_threshold_dim=i).to_numpy().flatten()\n", + " ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n", + "ax.set_xlabel(\"Probability\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.set_title(\"Cumulative Probability by Response Category\")\n", + "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice in the plots above, the jump in probability from category 3 to 4. Additionally, the estimates of the coefficients is precise for each category. Now that we have an understanding how the cumulative link function is applied to produce ordered cumulative outcomes, we will add predictors to the model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding predictors\n", + "\n", + "In the cumulative model described above, adding predictors was explicitly left out. In this section, it is described how predictors are added to ordinal cumulative models. When adding predictor variables, what we would like is for any predictor, as it increases, predictions are moved progressively (increased) through the categories in sequence. A linear regression is formed for $Z$ by adding a predictor term $\\eta$\n", + "\n", + "$$\\eta = \\beta_1 x_1 + \\beta_2 x_2 +, . . ., \\beta_n x_n$$\n", + "\n", + "where $\\epsilon$ is an error term. Notice how similar this looks to an ordinary linear model. However, there is no intercept or error term. This is because the intercept is replaced by the threshold $\\tau$ and the error term $\\epsilon$ is added seperately to obtain\n", + "\n", + "$$Z = \\eta + \\epsilon$$ \n", + "\n", + "Putting the predictor term together with the thresholds and cumulative distribution function, we obtain the probability of $Y$ being equal to a category $k$ as\n", + "\n", + "$$Pr(Y = k | \\eta) = F(\\tau_k - \\eta) - F(\\tau_{k-1} - \\eta)$$\n", + "\n", + "The same predictor term $\\eta$ is subtracted from each threshold because if we decrease the log-cumulative-odds of every outcome value $k$ below the maximum, this shifts probability mass upwards towards higher outcome values. Thus, positive $\\beta$ values correspond to increasing $x$, which is associated with an increase in the mean response $Y$. The parameters to be estimated from the model are the thresholds $\\tau$ and the predictor terms $\\eta$ coefficients. \n", + "\n", + "To add predictors for ordinal models in Bambi, we continue to use the formula interface." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = bmb.Model(\n", + " \"response ~ 0 + action + intention + contact + action:intention + contact:intention\", \n", + " data=trolly, \n", + " family=\"cumulative\"\n", + ")\n", + "idata = model.fit(random_seed=1234)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the summary dataframe below, we only select the predictor variables as the thresholds are not of interest at the moment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the summary dataframe below, we only select the predictor variables as the cutpoints are not of interest at the moment." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
action[1]-0.4660.055-0.563-0.3630.0030.002412.0645.01.00
intention[1]-0.2780.060-0.390-0.1670.0030.002379.0500.01.01
contact[1]-0.3270.072-0.460-0.2040.0030.002525.0688.01.00
action:intention[1, 1]-0.4500.080-0.609-0.3000.0040.003396.0479.01.00
contact:intention[1, 1]-1.2780.097-1.459-1.0980.0040.003557.0567.01.00
\n", + "
" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", + "action[1] -0.464 0.056 -0.567 -0.363 0.003 0.002 \n", + "intention[1] -0.278 0.060 -0.390 -0.167 0.003 0.002 \n", + "contact[1] -0.327 0.072 -0.460 -0.204 0.003 0.002 \n", + "action:intention[1, 1] -0.450 0.080 -0.609 -0.300 0.004 0.003 \n", + "contact:intention[1, 1] -1.278 0.097 -1.459 -1.098 0.004 0.003 \n", + "\n", + " ess_bulk ess_tail r_hat \n", + "action[1] 412.0 645.0 1.00 \n", + "intention[1] 379.0 500.0 1.01 \n", + "contact[1] 525.0 688.0 1.00 \n", + "action:intention[1, 1] 396.0 479.0 1.00 \n", + "contact:intention[1, 1] 557.0 567.0 1.00 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(\n", + " idata, \n", + " var_names=[\"action\", \"intention\", \"contact\", \n", + " \"action:intention\", \"contact:intention\"]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The posterior distribution of the slopes are all negative indicating that each of these story features reduces the rating—the acceptability of the story. Below, a forest plot is used to make this insight more clear." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_forest(\n", + " idata,\n", + " combined=True,\n", + " var_names=[\"action\", \"intention\", \"contact\", \n", + " \"action:intention\", \"contact:intention\"],\n", + " figsize=(7, 3),\n", + " textsize=11\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, we can plot the cumulative probability of each category. Compared to the same plot above, notice how most of the category probabilities have been shifted to the left. Additionally, there is more uncertainty for category 3, 4, and 5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3))\n", + "for i in range(6):\n", + " outcome = expit_func(idata.posterior.response_threshold).sel(response_threshold_dim=i).to_numpy().flatten()\n", + " ax.hist(outcome, bins=15, alpha=0.5, label=f\"Category: {i}\")\n", + "ax.set_xlabel(\"Probability\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.set_title(\"Cumulative Probability by Category\")\n", + "ax.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Posterior predictive distribution\n", + "\n", + "To get a sense of how well the ordinal model fits the data, we can plot samples from the posterior predictive distribution. To plot the samples, a utility function is defined below to assist in the plotting of discrete values." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def adjust_lightness(color, amount=0.5):\n", + " import matplotlib.colors as mc\n", + " import colorsys\n", + " try:\n", + " c = mc.cnames[color]\n", + " except:\n", + " c = color\n", + " c = colorsys.rgb_to_hls(*mc.to_rgb(c))\n", + " return colorsys.hls_to_rgb(c[0], c[1] * amount, c[2])\n", + "\n", + "def plot_ppc_discrete(idata, bins, ax):\n", + " \n", + " def add_discrete_bands(x, lower, upper, ax, **kwargs):\n", + " for i, (l, u) in enumerate(zip(lower, upper)):\n", + " s = slice(i, i + 2)\n", + " ax.fill_between(x[s], [l, l], [u, u], **kwargs)\n", + "\n", + " var_name = list(idata.observed_data.data_vars)[0]\n", + " y_obs = idata.observed_data[var_name].to_numpy()\n", + " \n", + " counts_list = []\n", + " for draw_values in az.extract(idata, \"posterior_predictive\")[var_name].to_numpy().T:\n", + " counts, _ = np.histogram(draw_values, bins=bins)\n", + " counts_list.append(counts)\n", + " counts_arr = np.stack(counts_list)\n", + "\n", + " qts_90 = np.quantile(counts_arr, (0.05, 0.95), axis=0)\n", + " qts_70 = np.quantile(counts_arr, (0.15, 0.85), axis=0)\n", + " qts_50 = np.quantile(counts_arr, (0.25, 0.75), axis=0)\n", + " qts_30 = np.quantile(counts_arr, (0.35, 0.65), axis=0)\n", + " median = np.quantile(counts_arr, 0.5, axis=0)\n", + "\n", + " colors = [adjust_lightness(\"C0\", x) for x in [1.8, 1.6, 1.4, 1.2, 0.9]]\n", + "\n", + " add_discrete_bands(bins, qts_90[0], qts_90[1], ax=ax, color=colors[0])\n", + " add_discrete_bands(bins, qts_70[0], qts_70[1], ax=ax, color=colors[1])\n", + " add_discrete_bands(bins, qts_50[0], qts_50[1], ax=ax, color=colors[2])\n", + " add_discrete_bands(bins, qts_30[0], qts_30[1], ax=ax, color=colors[3])\n", + "\n", + " \n", + " ax.step(bins[:-1], median, color=colors[4], lw=2, where=\"post\")\n", + " ax.hist(y_obs, bins=bins, histtype=\"step\", lw=2, color=\"black\", align=\"mid\")\n", + " handles = [\n", + " Line2D([], [], label=\"Observed data\", color=\"black\", lw=2),\n", + " Line2D([], [], label=\"Posterior predictive median\", color=colors[4], lw=2)\n", + " ]\n", + " ax.legend(handles=handles)\n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idata_pps = model.predict(idata=idata, kind=\"pps\", inplace=False)\n", + "\n", + "bins = np.arange(7)\n", + "fig, ax = plt.subplots(figsize=(7, 3))\n", + "ax = plot_ppc_discrete(idata_pps, bins, ax)\n", + "ax.set_xlabel(\"Response category\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.set_title(\"Cumulative model - Posterior Predictive Distribution\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sequential Model\n", + "\n", + "For some ordinal variables, the assumption of a **single** underlying continuous variable (as in cumulative models) may not be appropriate. If the response can be understood as being the result of a sequential process, such that a higher response category is possible only after all lower categories are achieved, then a sequential model may be more appropriate than a cumulative model.\n", + "\n", + "Sequential models assume that for **every** category $k$ there is a latent continuous variable $Z$ that determines the transition between categories $k$ and $k+1$. Now, a threshold $\\tau$ belongs to each latent process. If there are 3 categories, then there are 3 latent processes. If $Z_k$ is greater than the threshold $\\tau_k$, the sequential process continues, otherwise it stops at category $k$. As with the cumulative model, we assume a distribution for $Z_k$ with a cumulative distribution function $F$.\n", + "\n", + "As an example, lets suppose we are interested in modeling the probability a boxer makes it to round 3. This implies that the particular boxer in question survived round 1 $Z_1 > \\tau_1$ , 2 $Z_2 > \\tau_2$, and 3 $Z_3 > \\tau_3$. This can be written as \n", + "\n", + "$$Pr(Y = 3) = (1 - P(Z_1 \\leq \\tau_1)) * (1 - P(Z_2 \\leq \\tau_2)) * P(Z_3 \\leq \\tau_3)$$\n", + "\n", + "As in the cumulative model above, if we assume $Y$ to be normally distributed with the thresholds $\\tau_1 = -1, \\tau_2 = 0, \\tau_3 = 1$ and cumulative distribution function $\\Phi$ then\n", + "\n", + "$$Pr(Y = 3) = (1 - \\Phi(\\tau_1)) * (1 - \\Phi(\\tau_2)) * \\Phi(\\tau_3)$$\n", + "\n", + "To add predictors to this sequential model, we follow the same specification in the _Adding Predictors_ section above. Thus, the sequential model with predictor terms becomes\n", + "\n", + "$$P(Y = k) = F(\\tau_k - \\eta) * \\prod_{j=1}^{k-1}{(1 - F(\\tau_j - \\eta))}$$\n", + "\n", + "Thus, the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$ rather than continuing past it." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Human resources attrition dataset\n", + "\n", + "To illustrate an sequential model with a stopping ratio link function, we will use data from the IBM human resources employee attrition and performance [dataset](https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset). The original dataset contains 1470 rows and 35 columns. However, our goal is to model the total working years of employees using age as a predictor. This data lends itself to a sequential model as the response, total working years, is a sequential process. In order to have 10 years of working experience, it is necessarily true that the employee had 9 years of working experience. Additionally, age is choosen as a predictor as it is positively correlated with total working years." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " YearsAtCompany Age\n", + "1 10 49\n", + "3 8 33\n", + "4 2 27\n", + "5 7 32\n", + "6 1 59" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "attrition = pd.read_csv(\"data/hr_employee_attrition.tsv.txt\", sep=\"\\t\")\n", + "attrition = attrition[attrition[\"Attrition\"] == \"No\"]\n", + "attrition[\"YearsAtCompany\"] = pd.Categorical(attrition[\"YearsAtCompany\"], ordered=True)\n", + "attrition[[\"YearsAtCompany\", \"Age\"]].head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, the empirical probabilities of the response categories are computed. Employees are most likely to stay at the company between 1 and 10 years." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pr_k = attrition.YearsAtCompany.value_counts().sort_index().values / attrition.shape[0]\n", + "\n", + "plt.figure(figsize=(7, 3))\n", + "plt.bar(np.arange(0, 36), pr_k)\n", + "plt.xlabel(\"Response category\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.title(\"Empirical probability of each response category\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Default prior of thresholds\n", + "\n", + "Before we fit the sequential model, it's worth mentioning that the default priors for the thresholds in a sequential model are different than the cumulative model. In the cumulative model, the default prior for the thresholds is a Normal distribution with a grid of evenly spaced $\\mu$ where an ordered transformation is applied to ensure the ordering of the values. However, in the sequential model, the ordering of the thresholds does not matter. Thus, the default prior for the thresholds is a Normal distribution with a zero $\\mu$ vector of length $k - 1$ where $k$ is the number of response levels. Refer to the [getting started](https://bambinos.github.io/bambi/notebooks/getting_started.html#specifying-priors) docs if you need a refresher on priors in Bambi. \n", + "\n", + "Subsequently, fitting a sequential model is similar to fitting a cumulative model. The only difference is that we pass `family=\"sratio\"` to the `bambi.Model` constructor. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Only 250 samples in chain.\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [YearsAtCompany_threshold, TotalWorkingYears]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [2000/2000 02:28<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 250 tune and 250 draw iterations (1_000 + 1_000 draws total) took 148 seconds.\n", + "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n" + ] + } + ], + "source": [ + "sequence_model = bmb.Model(\n", + " \"YearsAtCompany ~ 0 + TotalWorkingYears\", \n", + " data=attrition, \n", + " family=\"sratio\"\n", + ")\n", + "sequence_idata = sequence_model.fit(random_seed=1234)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: YearsAtCompany ~ 0 + TotalWorkingYears\n", + " Family: sratio\n", + " Link: p = logit\n", + " Observations: 1233\n", + " Priors: \n", + " target = p\n", + " Common-level effects\n", + " TotalWorkingYears ~ Normal(mu: 0.0, sigma: 0.3223)\n", + " \n", + " Auxiliary parameters\n", + " threshold ~ Normal(mu: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n", + " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.], sigma: 1.0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sequence_model" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
YearsAtCompany_threshold[0]-2.5250.193-2.896-2.1910.0040.0032027.0812.01.00
YearsAtCompany_threshold[1]-1.0570.110-1.265-0.8500.0030.0021137.0601.01.01
YearsAtCompany_threshold[2]-1.0170.119-1.238-0.7920.0040.0031009.0705.01.00
YearsAtCompany_threshold[3]-0.7600.117-0.984-0.5530.0030.0021210.0833.01.00
YearsAtCompany_threshold[4]-0.7540.126-0.969-0.4930.0030.0021327.0780.01.00
YearsAtCompany_threshold[5]0.2510.1110.0530.4660.0030.0021207.0808.01.01
YearsAtCompany_threshold[6]-0.5000.145-0.769-0.2300.0040.0031394.0526.01.01
YearsAtCompany_threshold[7]-0.0850.137-0.3550.1550.0040.0031115.0835.01.00
YearsAtCompany_threshold[8]0.0250.141-0.2130.3100.0040.0041081.0750.01.00
YearsAtCompany_threshold[9]0.3890.1460.1280.6730.0040.0031134.0865.01.00
YearsAtCompany_threshold[10]1.2670.1540.9701.5400.0050.004923.0737.01.01
YearsAtCompany_threshold[11]0.4360.2130.0550.8260.0060.0041376.0662.01.00
YearsAtCompany_threshold[12]-0.1160.307-0.6620.4570.0080.0101568.0693.01.00
YearsAtCompany_threshold[13]0.5130.2500.0240.9430.0060.0051537.0738.01.00
YearsAtCompany_threshold[14]0.3920.293-0.1260.9580.0080.0061328.0696.01.01
YearsAtCompany_threshold[15]0.7910.2770.3101.3480.0080.0051405.0619.01.00
YearsAtCompany_threshold[16]0.4330.329-0.1961.0170.0090.0061516.0691.01.01
YearsAtCompany_threshold[17]0.2520.365-0.3970.9160.0090.0091468.0841.01.00
YearsAtCompany_threshold[18]0.7910.3300.1601.3940.0090.0071274.0856.01.00
YearsAtCompany_threshold[19]0.7880.3470.1481.4450.0100.0071306.0612.01.01
YearsAtCompany_threshold[20]2.1910.2681.6842.6800.0080.0061076.0720.01.00
YearsAtCompany_threshold[21]1.8420.3311.3232.5160.0090.0061447.0825.01.00
YearsAtCompany_threshold[22]2.4310.3691.7463.0780.0110.0081230.0616.01.00
YearsAtCompany_threshold[23]0.0170.743-1.3891.3400.0160.0272194.0712.01.00
YearsAtCompany_threshold[24]1.5810.5710.4892.5820.0150.0101671.0636.01.00
YearsAtCompany_threshold[25]1.5300.5790.4912.6460.0130.0102056.0616.01.00
YearsAtCompany_threshold[26]1.7270.6070.6052.8440.0140.0101982.0709.01.00
YearsAtCompany_threshold[27]1.0960.732-0.3512.4120.0170.0161855.0791.01.02
YearsAtCompany_threshold[28]1.1560.722-0.2882.3990.0160.0132073.0852.01.00
YearsAtCompany_threshold[29]0.5410.868-1.1492.2390.0190.0272055.0669.01.01
YearsAtCompany_threshold[30]1.2470.808-0.2752.7440.0190.0151821.0653.01.01
YearsAtCompany_threshold[31]1.3850.8130.0133.0210.0190.0151875.0599.01.01
YearsAtCompany_threshold[32]2.6830.7101.3183.9360.0170.0121698.0815.01.01
YearsAtCompany_threshold[33]0.8681.018-0.9742.9440.0260.0241573.0781.01.00
YearsAtCompany_threshold[34]1.7560.8530.3483.5000.0210.0171640.0876.01.01
TotalWorkingYears0.1270.0050.1180.1380.0000.000482.0514.01.01
\n", + "
" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean \\\n", + "YearsAtCompany_threshold[0] -2.525 0.193 -2.896 -2.191 0.004 \n", + "YearsAtCompany_threshold[1] -1.057 0.110 -1.265 -0.850 0.003 \n", + "YearsAtCompany_threshold[2] -1.017 0.119 -1.238 -0.792 0.004 \n", + "YearsAtCompany_threshold[3] -0.760 0.117 -0.984 -0.553 0.003 \n", + "YearsAtCompany_threshold[4] -0.754 0.126 -0.969 -0.493 0.003 \n", + "YearsAtCompany_threshold[5] 0.251 0.111 0.053 0.466 0.003 \n", + "YearsAtCompany_threshold[6] -0.500 0.145 -0.769 -0.230 0.004 \n", + "YearsAtCompany_threshold[7] -0.085 0.137 -0.355 0.155 0.004 \n", + "YearsAtCompany_threshold[8] 0.025 0.141 -0.213 0.310 0.004 \n", + "YearsAtCompany_threshold[9] 0.389 0.146 0.128 0.673 0.004 \n", + "YearsAtCompany_threshold[10] 1.267 0.154 0.970 1.540 0.005 \n", + "YearsAtCompany_threshold[11] 0.436 0.213 0.055 0.826 0.006 \n", + "YearsAtCompany_threshold[12] -0.116 0.307 -0.662 0.457 0.008 \n", + "YearsAtCompany_threshold[13] 0.513 0.250 0.024 0.943 0.006 \n", + "YearsAtCompany_threshold[14] 0.392 0.293 -0.126 0.958 0.008 \n", + "YearsAtCompany_threshold[15] 0.791 0.277 0.310 1.348 0.008 \n", + "YearsAtCompany_threshold[16] 0.433 0.329 -0.196 1.017 0.009 \n", + "YearsAtCompany_threshold[17] 0.252 0.365 -0.397 0.916 0.009 \n", + "YearsAtCompany_threshold[18] 0.791 0.330 0.160 1.394 0.009 \n", + "YearsAtCompany_threshold[19] 0.788 0.347 0.148 1.445 0.010 \n", + "YearsAtCompany_threshold[20] 2.191 0.268 1.684 2.680 0.008 \n", + "YearsAtCompany_threshold[21] 1.842 0.331 1.323 2.516 0.009 \n", + "YearsAtCompany_threshold[22] 2.431 0.369 1.746 3.078 0.011 \n", + "YearsAtCompany_threshold[23] 0.017 0.743 -1.389 1.340 0.016 \n", + "YearsAtCompany_threshold[24] 1.581 0.571 0.489 2.582 0.015 \n", + "YearsAtCompany_threshold[25] 1.530 0.579 0.491 2.646 0.013 \n", + "YearsAtCompany_threshold[26] 1.727 0.607 0.605 2.844 0.014 \n", + "YearsAtCompany_threshold[27] 1.096 0.732 -0.351 2.412 0.017 \n", + "YearsAtCompany_threshold[28] 1.156 0.722 -0.288 2.399 0.016 \n", + "YearsAtCompany_threshold[29] 0.541 0.868 -1.149 2.239 0.019 \n", + "YearsAtCompany_threshold[30] 1.247 0.808 -0.275 2.744 0.019 \n", + "YearsAtCompany_threshold[31] 1.385 0.813 0.013 3.021 0.019 \n", + "YearsAtCompany_threshold[32] 2.683 0.710 1.318 3.936 0.017 \n", + "YearsAtCompany_threshold[33] 0.868 1.018 -0.974 2.944 0.026 \n", + "YearsAtCompany_threshold[34] 1.756 0.853 0.348 3.500 0.021 \n", + "TotalWorkingYears 0.127 0.005 0.118 0.138 0.000 \n", + "\n", + " mcse_sd ess_bulk ess_tail r_hat \n", + "YearsAtCompany_threshold[0] 0.003 2027.0 812.0 1.00 \n", + "YearsAtCompany_threshold[1] 0.002 1137.0 601.0 1.01 \n", + "YearsAtCompany_threshold[2] 0.003 1009.0 705.0 1.00 \n", + "YearsAtCompany_threshold[3] 0.002 1210.0 833.0 1.00 \n", + "YearsAtCompany_threshold[4] 0.002 1327.0 780.0 1.00 \n", + "YearsAtCompany_threshold[5] 0.002 1207.0 808.0 1.01 \n", + "YearsAtCompany_threshold[6] 0.003 1394.0 526.0 1.01 \n", + "YearsAtCompany_threshold[7] 0.003 1115.0 835.0 1.00 \n", + "YearsAtCompany_threshold[8] 0.004 1081.0 750.0 1.00 \n", + "YearsAtCompany_threshold[9] 0.003 1134.0 865.0 1.00 \n", + "YearsAtCompany_threshold[10] 0.004 923.0 737.0 1.01 \n", + "YearsAtCompany_threshold[11] 0.004 1376.0 662.0 1.00 \n", + "YearsAtCompany_threshold[12] 0.010 1568.0 693.0 1.00 \n", + "YearsAtCompany_threshold[13] 0.005 1537.0 738.0 1.00 \n", + "YearsAtCompany_threshold[14] 0.006 1328.0 696.0 1.01 \n", + "YearsAtCompany_threshold[15] 0.005 1405.0 619.0 1.00 \n", + "YearsAtCompany_threshold[16] 0.006 1516.0 691.0 1.01 \n", + "YearsAtCompany_threshold[17] 0.009 1468.0 841.0 1.00 \n", + "YearsAtCompany_threshold[18] 0.007 1274.0 856.0 1.00 \n", + "YearsAtCompany_threshold[19] 0.007 1306.0 612.0 1.01 \n", + "YearsAtCompany_threshold[20] 0.006 1076.0 720.0 1.00 \n", + "YearsAtCompany_threshold[21] 0.006 1447.0 825.0 1.00 \n", + "YearsAtCompany_threshold[22] 0.008 1230.0 616.0 1.00 \n", + "YearsAtCompany_threshold[23] 0.027 2194.0 712.0 1.00 \n", + "YearsAtCompany_threshold[24] 0.010 1671.0 636.0 1.00 \n", + "YearsAtCompany_threshold[25] 0.010 2056.0 616.0 1.00 \n", + "YearsAtCompany_threshold[26] 0.010 1982.0 709.0 1.00 \n", + "YearsAtCompany_threshold[27] 0.016 1855.0 791.0 1.02 \n", + "YearsAtCompany_threshold[28] 0.013 2073.0 852.0 1.00 \n", + "YearsAtCompany_threshold[29] 0.027 2055.0 669.0 1.01 \n", + "YearsAtCompany_threshold[30] 0.015 1821.0 653.0 1.01 \n", + "YearsAtCompany_threshold[31] 0.015 1875.0 599.0 1.01 \n", + "YearsAtCompany_threshold[32] 0.012 1698.0 815.0 1.01 \n", + "YearsAtCompany_threshold[33] 0.024 1573.0 781.0 1.00 \n", + "YearsAtCompany_threshold[34] 0.017 1640.0 876.0 1.01 \n", + "TotalWorkingYears 0.000 482.0 514.0 1.01 " + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(sequence_idata)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The coefficients are still on the logits scale, so we need to apply the inverse of the logit function to transform back to probabilities. Below, we plot the probabilities for each category." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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C6dOn49FHH0WXLl3w7rvvIiwsDMuWLTN6//379yMyMhJz585FVFQUBg4ciMcffxyHDh2y8siJiExTqdFi3wXdbH5j6pM1F/1MmaVyyhoqbgsA3u4ueHRQFG5p64tWCjnKKgUqNA3n+DzYL6JWQKan3/QwqU84Zg1pDwB4d8d5VGoYmDVW2vUS7Kr64GBqgn91faqWwc+k5yOvuMKiY2tIr3BvuMrqvvocLc/QZkFZeXk5Dh8+jJEjR9Y4PnLkSOzbt8/oY/r3748rV65g8+bNEEIgIyMD3377Le644w5rDJmIqNESUq+joKwS3u4u6FZV08mWIv2rapVlFUGIpic/Vy9uezNJ1dfrd3fDi3dEY90/43F84Ui8cHsXk57b1CWnqf0j4ePuguSsIvyQcNXEkZPeNwdToRXALW19DTOpjdHaU4G2rT0ghPXrlf1382mU1xHgN7a4sj2wWVCWlZUFjUaDwMCaa9eBgYFIT083+pj+/ftj7dq1mDhxIlxdXREUFARvb2988MEHdb5OWVkZ8vPza3wRWRMbJzs3/dLlwPb+dvHGEObrDqkEKCrXIKvQMmUx9MVtb/7xjBW3lUolhoKjDTF1yamVQo7HB7cDALz/23lUcLbMZBqtwDeHdG2VGpPgfzNDXpkVg7J1B1Kw+q/LAICZg9shuInFle2BzXdf3lyvp76ChomJiZg7dy5eeukljBo1Cmq1Gs8++yxmzJiBFStWGH3M4sWLsWjRIouPm8gUbJxM+lIYt9pBPhkAKOQyhHi74UpuCS5lF6G1p8Iizxvf1h/6zxuv39MNEb4edRa31S95pueVGs0rk0D3htqYJacp8RH4bE8SUnKKsfHIFUw0YxnOGe06lwl1Xim83V0wqqv5FfD7Rvni6wOpVssrO3w5B//58SQA4OkRHTFnWAc8M6pTk4or2wObzZT5+/tDJpPVmhXLzMysNXumt3jxYgwYMADPPvssunfvjlGjRmHp0qX4/PPPoVYbLw63YMEC5OXlGb5SU1Mt/rMQGcPGyXS9uBzHr1wHAAzq6G/bwVQT2Qw9MBPVulWIMF83TOoTXm9xW0v289Rzd5VjhmG27ALKKzlbVh/9DP6S7ecAAP/o1QZKF5nZz9c3Sldu4mRaHorKKi0yxrqo80rw+JdHUKERGBMThNlDdTmFTS2ubA9sFpS5uroiNjYW27dvr3F8+/bt6N+/v9HHFBcXQyqtOWSZTHcR1ZUboVAo4OXlVeOLqLm1tIKGZJ69F7KgFUDHwFYIriraag+q55VZij4oiw427W+spfp5VvfQLRFo7alA2vUSw5Ic1bblpBoD3/gd9y/fj5Npuv9vm45dbdIHxTbebmjj7QaNVuBISq6lhlpLaYUGj395GFmFZegc5Im37+th0XZhtmbT3Zfz58/HZ599hs8//xynT5/GU089hZSUFMyYMQOAbpZrypQphvuPHTsWGzduxLJly5CUlIQ///wTc+fORd++fRESEmKrH4OoFjZOJgDYc872VfyNsXStMgBIvKoPykzfzDA6Jhh7nxuKrx+7Be9N6omvH7sFe58bavbSvtJFhidu082WffTHBZRWaMx6npasrhn87MLyJs/gN3e9MiEEnt94Asev5MHb3QXLp8TBQ2HzLCyLsulPM3HiRGRnZ+OVV16BWq1GTEwMNm/ejIiICACAWq1GSsqNXmfTpk1DQUEBPvzwQzz99NPw9vbG0KFD8cYbb9jqRyAyqqUVNKTGE0IYisbaQymM6gy1yppjpiykcasR+iUnS5nUNxyf7E6COq8U6w6kYNqAKIs9t6NraAZfAt0M/ojoILOW/vpG+WLj0bRmyytbsTcZG4+mQSaVYOkDvU1uBeVIbB5izpo1C7NmzTJ626pVq2odmzNnDubMmdPMoyJqmpZW0JAa70JmIdR5pXCVSw0zCPYiQl+rLLuo3s1Vpiqv1OJCZgGAxgdllqZ0keGJIe3x4g8n8dHOi5jUN7xJuVL2TqMVJie3N3dLIv3GjITU6yit0Fj0vO85fw3/3axrr/XiHV3Qv7395Ghaks2DMqKWqDl2l5Fj0e+67Bfla3dBQXhVWYzicg2uFZQhwKtpHw7OZRSgQiOgcnNBiMr2HzQmxIVh2c6LSLtegjX7L+PRQW1tPaRmYcru7uLySvydnIO957Pwy3HTariZO4Mf5e8B/1YKZBWW4fiVPIv9fbucXYTZXx2FVgD3xYZiWv9IizyvPbJ570uilqi+gpp6jlTQkBpPX5/MXkphVOcql6KNj27jgSWWMKsn+dtD0rWrXIo5VTvyPt51EcXlzbsb0Bbq2909Y80RzFt3FPd/uh89F23HwysPYsXeZKTnl5n03ObO4Eskkmp5ZZbpSV1YVonHVh9CXkkFeoZ547V/xNjFNdZcGJQRNZPRMcF4897utY7LJBIsfdCxChpS45RWaPB3sv20VjLGksn+hiR/Gy9dVndPbCjCfd2RVVhuKDDaUpiyu/uHhKv4Kykb5Rot2ni74f6+YfhgUi8EeirqbIlliZZE+sdaIq9MqxWYvz4B5zIKEeCpwCeTY6GQ29ess6UxKCNqRvo/kBG+7njr3u5wlUmhEcIwS0Et08FLOSit0CLQS4GOga1sPRyjDLXKLNCYvLHlMKzBRSbF3GEdAACf7LqIwmaunWVNDeWG6T08IBJ/PHMb9j43BIvv7o6xPUOwaFxXAJarD3czfVB2+HJuk/uQvvfbeWxLzICrTIpPJscisInL7I6AQRlRM9p2Slcc+Z7YUNwXF4YRXXWFkX8+zsKxLdme8zdKYdjrUkukv2UakwshcLpqpqxrG/sJygBgfM8QtPX3QG5xBb7Yd8mqr92c7dVMzfnqGeaNKH+PGtdgc9SHq65ToCe8lHIUl2tw6qr5bQ23nEzHe7+dBwD83z9i0Cvcp0njchRM9CdqJoVllYZkb337krHdg/HLcTV+Oa7GgjGd7fYNm5rGkE9mp0uXABBVVUC2qTllV3JLUFBWCVeZFO1a29esoFwmxZPDO+DJdQn4dHcSJsdHwEvp0uyv29zt1Zq6u3t0TDBGRAc1S0siqVSCvlG+2HE6EweSc9AjzLvRz3EmPR/zv0kAoJvtuy8urMnjchScKSNqJrvOXkN5pRaRfu6GJazbOgXAw1WGtOslOJJy3bYDpGaRkV+KM+kFkEh0TcjtVfWcsro6ophCPxvSMagVXGT295ZyZ/cQtA9ohbySCny+N7nZX88a7dX0u7vrYkpuWHO2JLqRV9b4ZP/rxeX45+rDKC7XoH87P7xwexeLjcsR2N9vEFELsaVq6XJUTJBhRkzpIsPwaN0S5i9cwmyR9EuX3dqo4OvhauPR1C3UR1cWo6RCg8wC03blGWOP+WTVyaQSzBuuyy37bHcSdiRmNMuSImC99mr17e62VG5YU/Sr6oN5IDkH2kb8rJUaLWZ/dRQpOcUI83XDRw/0htwOA/3mxOVLomZQVqnBH2cyAdxYutS7s3sIfky4is0n1Hjxji6QsixGi2LPpTCqc5VLEerjjpScYiRnFZmdRH2jvZJ9BmUAcHtMMNp4n0ba9VI8uvqQ4XhjlxSFEMgvqYQ6vwTpeaW6r/wb/71YVTC4zsejacVZqxvaORBeSjnyS2tuYAiy4DKpubqGeMHdVYb80kqczShAFxOvjcW/nsHeC1lwd5Vh+ZQ4+Njxh5rmwqCMqBnsu5CNwrJKBHop0DPUu8Ztt3b0h6dSjvT8Uhy6nMsCsi2IViuw94Jupsye88n0Iv09kJJTjEtZRbilrXlBwmlDeyXTe15a27bEdKRdrx0s6ZcUlz3UGyOig5BdWAb1TYFWel4p1HklyMgvgzqvBKUVTdtRCFimvdqvJ9XIL61E61auWDKhJ3KKyy2aG9YUcpkUsRE+2HM+CweSc0wKyr47fAUrqpaX/3dfD3QOst8gvzkxKCNqBlurli5HRgfVmglTyGUYGR2E745cwc/HrzIoa0FOXc1HTlE5Wink6BXubevhNCjKzx27AVwys1bZ9eJypF0vAQB0Dva04MgsR7+kaIx+YW3W2iMAAFNX2nzcXRDopUSwSokglRJBXm4IUimQW1yB13890+DjLdFe7fM/LwEAJsdHYpAdfgDoF+VrCMqmNlCBPyH1OhZ8fwIAMHdoe4zp5rw1HBmUEVmYRiuwPTEDADA6Jsjofe7sEYzvjlzB5hPpWDi2q80/2ZJl6BuQx7fzs8uk95sZemCauQNTn08W7utulV2N5jClppc+GJNKdAFToEqJYK+qgEulC770QVigl7LOtlkarcAX+y41e3u1oym5OJZ6Ha4yKR7oF96k52oufavyyv5Ozqm3v2pmfike//IQyiu1GN4lEPOGd7TmMO0OgzIiCzt0KQfZReVQubnU+cd3YHt/eLu7IKuwDH8nZbfY5rrOZpcDlMKoLsr/RmNyczhCPpmpS4Wv3NUVD/QLb1JiuT4Bf+aaI5AARgMzSyTgr6yaJburZwj8Wyma9FzNpXuoCq5yKbIKy5CcVYS2RsqllFVqMGPNYWTkl6F9QCu8M7GH0+fY2v9HOSIHs/WUbpZsWJeAOmdLXGRSjK7aALCJuzBbhMKyShy5nAsAuLWDYwTZkdWCssbsktOzx/ZKNzN1qbBDoKdFdvrVVZwVAJ4d1anJCfjpeaXYfEL3N8OeG3MrXWToWVWj7ICRlktCCLz0wykcSbkOL6Ucy6fEwdNOZ1utiUEZkQUJIQz5ZKO7Gl+61LuzewgAXV2jiia2IyHb++tiNiq1AhF+7oZlQXsX6uMGmVSC0gqtWWUx7L0cBnCjpldz9nu82eiYYOx9bii+fuwWvDepJ27rqAvS911sepPuNfsvo1Ir0DfSFzFt7HdzBYBqzclrB2Wr/7qM9YdSIZUAHzzQ2zBr6+wYlBFZ0Kmr+Ui7XgI3F1mDS1i3tPWFn4crcosrLPLH2hE0Z+sZSzBnfPrH6Nv42HPB2Ju5yKQIrerD2tjK/qUVGlzILARg3zNl1Wt6NVe/x7peV1+c9dXx3eAik2DvhSz81YTf9dIKDb46kAJAV+ne3tXVnPyvi9l45Wfd5ot/j+mMwQ6y3G8NzCkjsqAtJ3WzZLd1al1nMrCeXCbFmG5BWLM/BT8fu9ri/zA1d+uZpjJnfMYes/mEGoM6+NvFz2SKSD8PXM4uxqXsokbVzrqQWYhKrYCPu0u91eXtgX5J8eb/V9aq6RXm645JfcLx5f7L+N+2s9gwI96sFms/HbuKnKJytPF2w4iqItT2rHe4D6QSIO16CVb+mYzOQV4IVikxa+1haLQC43uG4LFBbW09TLvCoIzIgvRLlzcXjK3LHd1CsGZ/CraeSsf//aMbXOUtc/Ja33rm5nmn6nWibBnEmDO+uh5zvbjCLn4mU0X5e2DXuWuN3oFZPZ/MEXq4Nme/R1PMHtoe3xxKxaHLudh17hpu6xTQqMcLIQwJ/pPjIxyi0v2e89cgk0qg1dwoSyKXSlCpFejWRoXX7+nuENeONTEoI7KQi9cKcT6zEHKpBEM6m/YHt2+UL1p7KnCtoAx7zl/DsC72/+m3sRpqPSOBrvXMiOggm5QGMaU1zr++PY5rhWVQyGSQyySQSiR4edMpu/2ZGiPST9eYvLE7MB0hn+xm+iVFWwj0UmJKfASW70nG/7adw+COrRsVkBxIzsFpdT6ULlJM6mP/Dbrr+tBSWZUScH/fsAZXE5yR/YfaRA5CP0vWv70/VG6m7SKSSSW4o6pQ4s8tdBdmQ3WiqreesQVT6ljll1biPz+cwr++O4753xzDvPUJuF5cUef9bf0zNUaEfgdmVuMKyDrCzkt7M2NwO7i7ynAiLc+wS9tU+lmyu3uHwtvdvtsP1fdBR++D3y/YXU6pPTArKFu1ahWKi82rAE3UUun/yI7q2rjZrju764Ky7YkZKK3QWHxctmZqnShLtJ4xh6mv262NF4Z2DsCgDv7oEFC75lJTntuWovwaXxZDqxXVZsrsewegPfFrpcAjA6IAAEu2nzU5KEnNKca2RN2HPnsug6FnygcdR/nQYm1mBWULFixAUFAQpk+fjn379ll6TEQOR51XgmOp1yGRoNEJuL3DfRCsUqKwrNJQfLQlMbVOlCVaz5jD1Nd9/vZofD6tD76c3g+vjIux6HPbUqiPG+RSCcoqtUjPNy2IvJJbgsKySrjKpWjbmqUMGuOxW9vCSynHuYxC/Hz8qkmP+XL/ZWiFbmdvx0D7bGdVnb1/ELNnZgVlV65cwZo1a5Cbm4shQ4agc+fOeOONN5Cenm7p8RE5hG1Vs2Sx4T6NfiOWtvAlzL5RvvBvVf9yi6XrRDWGvo5VXYzVsbJF7avmIpdJEebbuLyyRHUeAKBToKdDtJOyJyo3F/zzVt2Ow3d3nEdlAzUKi8srsc6BymAA9v9BzJ6Z9dskk8lw1113YePGjUhNTcU///lPrF27FuHh4bjrrrvw448/QqtlMUxyHvpSGHX1umzInT10hWR/O52BkvKWt4Tpqax/T9ETQ9rbLCFeJpXgmVGdjN5WVx0rW9W+ai4R+mR/E/PKHKG9kj2bNiAKvh6uSM4qwsYjafXed+ORNOSXViLCzx1DGrlj01Za0ocWa2vyR5yAgAAMGDAA8fHxkEqlOHHiBKZNm4Z27dph586dFhgikX3LKSrHgUu63AhTS2HcrEeoCmG+bigu1+D3M5mWHJ7NfbHvEpKziqGUSxHgWbNPn4tM92f7x4S0BmcMmlNG1bKd/KYgKkilrLO0RV3tdOp7jL2K9GtcD0xDPhmT/M3SSiHHrNvaAQDe++08yiqNfxATQmBVVVHiqfGRDtMXsqV9aLEms0tiZGRk4Msvv8TKlSuRlJSE8ePH4+eff8bw4cNRUlKCF198EVOnTsXly5ctOV4iu7PjdAY0WoEuwV6GZaDGkkgkuKNbCD7edRE/H7+KO7o7zht6fVJzivHW1rMAgP+MjcakPuE16kQFeSkx9sO9OHgpF8t2XsScYR2sPsbSCo1hZ9t/7+6GMB93k+tY2br2laXoW9yYWtWfOy+b7qFbIvDp7iSkXS/B+oOpmBIfWes+ey9k4UJmIVop5LgvLtT6g2wCWxfsdVRmBWVjx47F1q1b0bFjRzz22GOYMmUKfH1vTEO6ubnh6aefxjvvvGOxgRLZq20m9rpsyJ3dg/Hxrov4/UwmCssq0Urh2GUEhRBYsPEESio06Bfli/v7hENqpE7UK+O6Yv43x/Dub+cxoIM/eof7WHWc3x9Nw7WCMgSrlBjfs02jC/jasvaVpRgak5sQlOUWleNq1Zts5yD7Tzq3V0oXGeYMbY///HgKH/x+AffFhsHNtWbdLv2HhXtjQx2yWXdL+dBiTWYtXwYEBGDXrl04efIk5s2bVyMg0wsODkZycnKTB0hkzwrLKrH7fBYAYFRM0wq/dg3xQqSfO8oqtfjtdONqGNmjDYeuYO+FLCjkUrxxT/c6l17+0asN7uoRAo1WYN66BBSWVVptjBqtwPLdSQCA6QOjWmxHhYboC8hezilusCyGfukyws/dIQMFezKxTzjaeLvhWkEZ1uyvuaqUnFWE389kQiIBpjpAGYy6VO8BGt/OjwFZA8z6CzR48GD07t271vHy8nKsXr0agG45JiIiommjI7Jzu85eQ3mlFpF+7ujUxK3qEokEd3bXJfxvOubYuzAz8kvx6i+6tipPj+xomIkxRiKR4NXxMWjj7YaUnGIs/PGUtYaJ7YnpSMoqgpdSjkl9w632uvamjbeuLEZ5pRbqBspi6Jcuu3Lpsslc5VI8OVy3ZL9s18UaH0j0De6HdAowLC9Ty2dWUPbwww8jLy+v1vGCggI8/PDDTR4UkaOo3uvSEj3c7uyhy7PYfe4a8krqrhhvSRqtwF8Xs/FjQhr+upjd5CrbQgj854eTKCitRI9QlaFYZn1Ubi54Z2JPSCXAd0euYNMx0+o3NXWcy3bpZsmmxEc6/HJxU8hlUoTry2I0sITpiO2V7Nndvdogyt8DOUXlWLlXt7pUUFqBDYdSAThOGQyyDLOCMiGE0TegK1euQKVidWdyDmWVN3ZKjjKzFMbNOgV6on1AK5RrtNie2PxLmFtOqjHwjd9x//L9eHJdAu5fvh8D3/gdW06aP1O3+UQ6tiVmwEUmwRv3dje5cXLfKF88MaQ9AOD5708g7XqJ2WMwxd/JOTiWeh2ucqlDLw9ZSqSJyf5M8rcsuUyKeVWzZZ/svogdiRlY+OMpFJVr0K61Bwa297fxCMmaGhWU9erVC71794ZEIsGwYcPQu3dvw1ePHj0waNAgDB8+vLnGSmRX9l3MRmFZJQI8FegZ6m2R59QtYeoLyTbvbJG+YfDN7VDS80oxc80RswKz3KJyLPzpJABg1m3t0TmocW/cc4d1QM8wbxSUVuKp9QnN2hvv410XAQD3xYai9U2lOpyRvlbZ5XrKYpRWaHDhWiEAtleypLHdQxCiUqKwTINHVx/CxqO62mXXCsoMs/HkHBo1Xz9+/HgAQEJCAkaNGoVWrW70f3N1dUVkZCTuueceiw6QyF5trSoYO7JroEXrB93ZPQTv7jiPveezkFtUDh8Pyzcfrq9hsICultCiTYkYER3UqMTcV39ORFZhOToGtsKsIe0aPS4XmRTvTeqJ29/bgwPJOfh410XD7JklnVbnY+fZa5BKgMcGtbX48zuiG2Ux6i4gez6jEBqtgK+HKwK9GMhayrbEdMOO1uoKSisxc80Rh6t7R+ZrVFC2cOFCAEBkZCQmTpwIpZItEsg5abTCsLw4uqtl/1i2D2iFzkGeOJNegKU7LyCmjcriW8kbahgscKNhsKnlHv44m4mNR9MglQBv3NMdCrms4QcZEeHngUXjYvDMhmN4Z/s53NLWD+WVWotuqf+0asflmJjgejchOBNTCsjq2ytFB3tZJIeSbnxAMqYpH5DIMZmV2Tp16lRLj4PIoRy6lIPsonKo3FzQr63lW4V0qgrKlu+5UVYm2IJFF01tBJyaW4x4NByUFZRW4IWNJwAAjwyIQq8m1hq7p3cb/HE2E78cV2PCJ3/VWMZs6nm4kluMn6o2Ejw+mLNkevqZspTsYmi0wmgAwHwyy2uOD0jkuEzOKfP19UVWlq4ek4+PD3x9fev8aoylS5ciKioKSqUSsbGx2LNnT733LysrwwsvvICIiAgoFAq0a9cOn3/+eaNek8hc+p2KS3fq8pGGdm5t8YbMW06q8WNC7XyypuR63czURsCLfjqFd3ecw/Xi8lq3Vd+1OX/9MVzNK0W4rzueHmm8j2RjSCQSDK3q83dzXllTz8OKvcnQaAX6t/NDdwvlArYEwSolXGQSlGu0UOcZ32TBnZeWZ+oHJFPvR47N5Jmyd955B56enoZ/W2Lqev369Zg3bx6WLl2KAQMG4JNPPsGYMWOQmJiI8HDjNYMmTJiAjIwMrFixAu3bt0dmZiYqK61XbJKc15aT6lotQ3aevYYtJ9UWy/ew1lKGvmFwel6p0bwyQFf0sahcg3d3nMfy3Ul4KD4C0wdGIcBTafRcAMDdvdvUqkpuDo1W4O1tZ43e1pTzkFtUjnUHdKUGZgxufM5bSyaXSRHm646ka0W4lFWMUJ+aLcO0WoHT6gIAnCmzJFM/IJl6P3JsJgdl1Zcsp02bZpEXX7JkCaZPn45HH30UAPDuu+9i69atWLZsGRYvXlzr/lu2bMGuXbuQlJRkmJGLjIy0yFiI6qPfqXhzAHO9uMKiibjWWsrQNwyeseZIrdv0Ic4Hk3pBC4GP/riI0+p8fLIrCav+vIRb2vph17lrRp/3vR3n0TnIs8nnornOw+q/LqOkQoPoYC8M6sBSAzeL8vNA0rUiJGcXYeBN5yc1txiFZZVwlUvRlnl4FtPQByQJdP0i+0ZZPk2C7I/J6y75+fkmf5mivLwchw8fxsiRI2scHzlyJPbt22f0MT/99BPi4uLw5ptvok2bNujYsSOeeeYZlJTUXc+orKzMrPER6TW0UxHQzdpYonyDNZcyRscEIzbCu9bxIJUSyx7qjdu7B+PO7iHYPHcgVkyNQ69wb5RVausMyPQscS6a4zyUlGvwxV+XAOhyyZioXlt9PTD1+WSdgzxNrj1HDdN/QAJufCDS03+/cGw0k/ydhMkzZd7e3g3+EdMXldVoNA0+X1ZWFjQaDQIDa/YLDAwMRHq68bosSUlJ2Lt3L5RKJb7//ntkZWVh1qxZyMnJqTOvbPHixVi0aFGD4yGqizUTca25lJFTVI4TV3RvtP/9Rww8FHKjuxslEgmGdQnE0M4BWLE3Ga/9crrO57TUuWiO87DhcCpyisoR6uOGO7qxvIAxkfXUKtPnk7G9kuWNjgnGsod610oJCLLg5h5yDCYHZX/88UezDODmQK+ubgEAoNVqIZFIsHbtWkPngCVLluDee+/FRx99BDc3t1qPWbBgAebPn2/4Pj8/H2FhYRb8Cails+bslTWXMr4/moZyjRYxbbzwQL+G+9RKJBKTi6w29VyYkvMml0rg7W5aQ+xKjRbL9+jKYDw2qC1neupQX1X/U1eZ5N+cRscEY0R0EA4k51i0/As5FpODssGDB1v0hf39/SGTyWrNimVmZtaaPdMLDg5GmzZtarRy6tKlC4QQuHLlCjp06FDrMQqFAgoFixyS+aw5e6Vfypi55ggkgNGAxBJLGUIIrD+YAgCY2Mf0RtzWOhemnIdKrcA/lv6JV8fF4L64+j9obT6ZjtScEvh6uGJCA/d1ZvpaZak5JbXKYrAcRvOTSSUse+HkTP64ePz4cWi1WsO/6/syhaurK2JjY7F9+/Yax7dv347+/fsbfcyAAQNw9epVFBYWGo6dO3cOUqkUoaGhpv4oRI3SN8oXQV51BxkS6MoJWCoRV7+UEaSq+ZoSAO9M7GmRpYyjqddxLqMQShcp7uoRYvLj9DNYdYWEljwXdZ2HYJUSb97bHYM6+KO0Qotnvz2Op785huJy47uwhRD4uKqEydT4SIvsDm2pQrzd4CqTolyjxdVqvUezC8uQnl8KiQTo1MjWWURkOpNnynr27In09HQEBASgZ8+ekEgkEKL251dTc8oAYP78+Zg8eTLi4uIQHx+PTz/9FCkpKZgxYwYA3dJjWloaVq9eDQB44IEH8Oqrr+Lhhx/GokWLkJWVhWeffRaPPPKI0aVLsi2NVrSIqXiZVILbOrXGuoOptW5rrkTc6ksZGfmleOPXM1DnlxqtF2aO9VVlIW7vFgyVm2lLgED9M1jNcS7qW9K5t3colu68gCXbz+G7I1dw7Mp1LH2wNzoG6kr36K+/PeevIVGdD6VciinxDS/TOjOZVIIwXzdcvFaES9lFCPPV5ZjpS2FE+nmglcKsmuNEZAKTf7uSk5PRunVrw78tYeLEicjOzsYrr7wCtVqNmJgYbN68GRERuj+carUaKSkphvu3atUK27dvx5w5cxAXFwc/Pz9MmDABr732mkXGQ5ZjrI6VJSvSW1NKdrGhmKuXUo780hszMs2ZiFt9KaOgtAL/+fEUVu67hMnxkU0KegrLKrGpqtn5pEYsXepZOym5riUdqVSC2UM7IC7SF3O/PooLmYW468O9eHVcDDyV8lrjk0ol+Ds52+GuP2uL8vfQBWVZRRjUQfc3v3p7JSJqPhJhbLqrBcvPz4dKpUJeXh68vPgHpjnUVdNLH0Y4UnNdrVZg0vL9OJCcg1va+uLLR/rh0OVcq8/+FZdXIn7x78grqcCnk2MxsmuQ2c/19YEULNh4Am1be+C3+YPNLg1hTzOhWYVleGp9Avacz6r3fhI41vVnC6/9nIjP9ibjkQFReKmqVMO8dUfxQ8JVPDuqU7M0iCdq6UyNPczegnT27FnMnj0bw4YNw/DhwzF79mycPWu8Ajc5D2vW9LKGL/66hAPJOXB3leGte3vARS5FfDs/jOvZBvHt/KwWhLi7ynF/X92s1oq9TZup1i/DTuoT1qRaXfoZLGufC2P8WynwxcN9MX9E7c0+N3Ok688WDLXKqpXFYHslIuswKyj79ttvERMTg8OHD6NHjx7o3r07jhw5gpiYGGzYsMHSYyQH0piaXvYuOasIb2w5AwB4/vYuhvwaW5naPwJyqQR/J+fgZFqeWc9xWp2PY6nXIZdKcHfvlrU5RiqVoE9k/TvXHOn6sxX9Dkx9AdnSCg0uXtP9mzsviZqXWUHZv/71LyxYsAB//fUXlixZgiVLlmDfvn14/vnn8dxzz1l6jORAWkpzXY1W4NkNx1BaocXA9v54sF/jc68sLVjlhturip5+buZs2fqqWbIR0YHwb9XySsW0lOvPliL9dR8+UnOLUanR4lxGATRaAT8PVwSYWKeOiMxjVlCWnp6OKVOm1Dr+0EMP1VmNn5xDS2muu/LPZBy6nItWCjlev6eb3bTkmT4wCgDw07GryMhvXGBRWqHB90fTAAAT+7TMWl0t5fqzpRCVG1zlUlRoBK5eL61Rn8xefg+IWiqzgrLbbrsNe/bsqXV87969GDRoUJMHRY7LmnWsmsuFzEK8tVWXH/niHV0Q6mPbZcvqeoR5Iy7CB5VagdVVfRxNtfVUOvJKKhCiUhp21bU0LeH6szWpVIKIqqX65Owi5pMRWZHJJTF++uknw7/vuusuPPfcczh8+DBuueUWAMD+/fuxYcMG9pl0cvo6VjPWHKl1myM019VoBZ7ZcAxllVrc2rG1Xc4oPTooCocu52Lt3ymYPaSDycVQ11XVJrsvLsxuz39TWbuOWksV4eeB85mFuJRVxEr+RFZkclA2fvz4WseWLl2KpUuX1jj2xBNPGIq/knMaHROMMTFB+PVkzaVsb3cXLL67m12XI1i+JwkJqdfhqZTjDTtatqxuRHQQwnzdkJpTgo1Hr+BBE/pWXsoqwl9J2ZBIgAl2GGhaEps7N11UVV5ZclYRTrMROZHVmByU6VssEZkiu1BXef6ft0bhtLoAe85nYVjnALt+QzyfUYAl284BAF66MxrBKvvsEiGTSjCtfxRe/TkRn+9Nxv19wiFtYObnm0O6WbJbO7RGG2/7/Lksic2dm0ZfFmP3uWsoKtdA6SJFlH8rG4+KqOUzu04ZUV1KKzRISL0OALi/bwRmDm4HAPjtTCYqNfYZ3FdqtHh6wzGUa7QY2jkA98bad7mICXGhaKWQ4+K1Iuw6f63e+1ZqtNhw+AoAXW0yZ2FPddQcTVRVWYykqrIYnYK8eP6IrMDsJmZFRUXYtWsXUlJSUF5esx/f3LlzmzwwclzHUq+jXKNFa08FIv3cEebjBm93F+QWV+DgpVyjLXNsoXpF+n0Xs3H8Sh68lHIsvts+ly2r81S6YGKfMKzYm4zP9yZjSKeAOu/7x9lruFZQBj8PVwzrEmjFUZKjiqiaKdPz83CFRisYmBE1M7OCsqNHj+L2229HcXExioqK4Ovri6ysLLi7uyMgIIBBmZPTF+bsG+ULiUQCuUyCYZ0D8d2RK9h6Kt0ugjJjvTkB4J7YNgj0coxyCdP6R2Lln8nYcz4LZ9ML0CnI0+j91h3Q9Y+9JzYUrnJOjlPDjqVcr/H972cyMfCN35mTR9TMzPoL/dRTT2Hs2LHIycmBm5sb9u/fj8uXLyM2NhZvv/22pcdIDubAJV1Q1q9a2YFRXXUzNNsTM2Drdqv63pzGOg+s+vMytpxU22BUjRfm645RVT0w6yomm55Xij/OZgIAJsQ5z9IlmW/LSTWe+Kr27un0vFLMXHPEYX4/iByRWUFZQkICnn76achkMshkMpSVlSEsLAxvvvkmnn/+eUuPkRxIhUaLw5dzAQD9om7MiN3asTXcXGRIu16Ck2n5thpevb059RypN6K+mOz3CWnIKiyrdfu3h1OhFUCfSB+0D2CiNtWvpfWuJXI0ZgVlLi4uhpybwMBApKTolkdUKpXh3+ScTl3NR3G5Bt7uLuhQLQhQusgwuKOuYOnWU7br+tCSenMCQGyED3qEqlBeqcXa/TV/97RagfWH9M3Hbd8miuxfS/v9IHI0ZgVlvXr1wqFDhwAAQ4YMwUsvvYS1a9di3rx56Natm0UHSI7lQHI2AKBPpG+tMg2jYnRLmLYMylJyiky6n6P0RpRIJJg+qC0A4Mv9l1BaoTHc9ldSNlJzSuCpkBt6ZhLVh71DiWzLrKDsv//9L4KDdX/kX331Vfj5+WHmzJnIzMzEp59+atEBkmP5O6l2Ppne0E6BkEslOJ9ZiKRrhVYdV0FpBT747Txe2ZRo0v0dqTfimJggBKuUyCosx0/HrhqOr6tqPj6uV4jJVf/JubF3KJFtmbX7Mi4uzvDv1q1bY/PmzRYbEDkujVYYkvyN9RZUubsgvp0f9pzPwtZTGZh5m2VynKqXtri5SGhBaQVW/XkJn+1NRl5JBQBd/aq6cmIk0FV+d6TeiC4yKab2j8Trv57B53uTcV9sKHKLK7C1qqMCly7JVPreoel5pUbzyhzx94PIkZhdpwwAMjMzcfbsWUgkEnTq1AmtW7fMJsdkmrPpBSgorYSHq6zO5sUjuwZVBWXpmHlbuya/prHSFsEqJZ4d1QlpuSU1grF2rT0wd1gHuEilht1lLaU34v19wvHejvM4k16Az/YkI1Gdj3KNFtHBnohpo7L18MhBsHcokW2ZtXyZn5+PyZMno02bNhg8eDBuvfVWhISE4KGHHkJeXp6lx0gOQp9PFhvpC7nM+KU1MlqXV5aQeh3p9SQUm6Ku0hbqvFLM/+YY/rf9HPJKKtA+oBXev78Xtj01GON6tsHt3XW9EYNUNZdgglRKLHuot0PWYVK5uxhmL/5v82l8fzQNAHAlt4QlDKhR9L1DW9LvB5GjMGum7NFHH0VCQgJ+/vlnxMfHQyKRYN++fXjyySfx2GOP4ZtvvrH0OMkBGKtPdrNALyV6hXvjaMp1bE9Mx+T4SLNey5TSFnKpBG/f1wNje4TU+mTf0nojbjmpxq5ztdstFZRWYuaaI3wzpUZpab8fRI7CrKDsl19+wdatWzFw4EDDsVGjRmH58uUYPXq0xQZHjkMIUaOSf31GdQ3C0ZTr2Hoqw+ygrKGt+wBQqRUI9FLW+Uai743o6PQBqjECumWnRZsSMSI6iG+qZLKW8vtB5EjMWr708/ODSlU7T0WlUsHHx6fJgyLHk5RVhKzCcijkUnQPrT+HSV+Ffn9SNvKKK8x6PW7dv4G1pYiIWgazgrIXX3wR8+fPh1p9I1clPT0dzz77LP7zn/9YbHDkOPRv+L3CvaGQ119+IcrfAx0DW6FSK/DbmQyzXo9b929ggEpE1DKYvHzZq1cvQxV/ADh//jwiIiIQHq7bbp+SkgKFQoFr167h8ccft/xIya7dWLo0bbljVNcgnMu4gK2n0nF379BGv55+635dM0TOtHWfASoRUctgclA2fvz4ZhwGOTIhBP5O0u28rC/Jv7pRXYPwwe8XsOvcNZSUaxpd3FQmleDpER3xzLfHa93mbFv3WVuKiKhlMDkoW7hwYXOOgxzYldwSXM0rhVwqQa9wb5Me0zXEC2283ZB2vQS7z18z5Jk1xok0XfkVuVSCymrFYINUSiwcG+00uw1ZW4qIqGVoUvHYw4cP4/Tp05BIJIiOjkavXr0sNS5yIPqly26hKri7mnZJSSQSjOwaiJV/XsLWU+mNDsrOZRRgzd+6BtyrpvWBTCZ16q37+tpSNxfSdbYAlYjIkZkVlGVmZmLSpEnYuXMnvL29IYRAXl4ehgwZgnXr1rGyv5MxtRTGzUZ1DcLKPy/ht9OZqNBo4VJHwdmbCSHw6s+J0GgFRnUNxMCOvN4A1pYiInJ0Zu2+nDNnDvLz83Hq1Cnk5OQgNzcXJ0+eRH5+PubOnWvpMZKdM6VorDF9In3h6+GKvJKKRpVr2HE6E3vOZ8FVJsULt0c36jVbOn1tqXE92yC+nR8DMiIiB2JWULZlyxYsW7YMXbp0MRyLjo7GRx99hF9//dVigyP7l5lfiuSsIkgkQFxk44IymVSC4V0CAABbT6Wb9JiySg3+7xddodTpg6IQ7ufeuAETERHZKbOCMq1WCxcXl1rHXVxcoNVqmzwochz6WbLoYC94KWtfEw3R55JtO5UBrba+pkk6q/68hEvZxWjtqcATQ9o3+vWIiIjslVlB2dChQ/Hkk0/i6tWrhmNpaWl46qmnMGzYMIsNjuyfuflkegPa+8PDVYb0/FIcT6u/mf21gjJ88PsFAMC/RnVCK0WT9qkQERHZFbOCsg8//BAFBQWIjIxEu3bt0L59e0RFRaGgoAAffPCBpcdIdkwflDU2n0xP6SLDbZ1MW8J8e+tZFJZVonuoCveYUXCWiIjInpk11RAWFoYjR45g+/btOHPmDIQQiI6OxvDhwy09PrJjuUXlOJNeAECXtG+ukV0D8csJNbaeSsdzozsbvc/JtDx8czgVgK7mlpQJ7ERE1MI0eqassrIScrkcJ0+exIgRIzBnzhzMnTvX7IBs6dKliIqKglKpRGxsLPbs2WPS4/7880/I5XL07NnTrNelpjtYlU/WPqAV/FopzH6eIZ0D4CKTIOlaES5kFtS6XQiBRZtOQQjgrh4hiI1gZXoiImp5Gh2UyeVyREREQKPRNPnF169fj3nz5uGFF17A0aNHMWjQIIwZMwYpKSn1Pi4vLw9Tpkxh/pqNNTWfTM9L6YL+7fwBAFtP1W5Q/ssJNQ5eyoXSRYp/jzE+k0ZEROTozMope/HFF7FgwQLk5JheW8qYJUuWYPr06Xj00UfRpUsXvPvuuwgLC8OyZcvqfdzjjz+OBx54APHx8U16fWoac+uTGaPfhXlzXllphQaLN58BAMwY3A4h3m5Nfi0iIiJ7ZFZQ9v7772PPnj0ICQlBp06d0Lt37xpfpigvL8fhw4cxcuTIGsdHjhyJffv21fm4lStX4uLFi+zFaWOFZZU4WbVb0hKNrkdEB0IiAY5fycPV6yWG45/uTkLa9RKEqJR4/NZ2TX4dIiIie2VWov/48eMhkUggRMN1peqSlZUFjUaDwMDAGscDAwORnm58F9758+fx73//G3v27IFcbtrQy8rKUFZWZvg+Pz/f7DHTDYcv50IrgHBfdwSrmj571dpTgdhwHxy6nIttp9IxbUAU1HklWLbzIgDg37d3gZurrMmvQ0REZK8aFZQVFxfj2WefxQ8//ICKigoMGzYMH3zwAfz9/c0egERScxedEKLWMQDQaDR44IEHsGjRInTs2NHk51+8eDEWLVpk9vjIuAPJ2QAsM0umN6prEA5dzsU3h67Ax8MV3xxMRUmFBnERPhjbnQ21iYioZWvU8uXChQuxatUq3HHHHbj//vuxY8cOzJw506wX9vf3h0wmqzUrlpmZWWv2DAAKCgpw6NAhzJ49G3K5HHK5HK+88gqOHTsGuVyO33//3ejrLFiwAHl5eYav1NRUs8ZLNVkqyb86pYtuJixRnY8n1yXgz4u6wE+3tMkSGERE1LI1aqZs48aNWLFiBSZNmgQAePDBBzFgwABoNBrIZI1bWnJ1dUVsbCy2b9+Of/zjH4bj27dvx7hx42rd38vLCydOnKhxbOnSpfj999/x7bffIioqyujrKBQKKBTml2ug2korNDiWqssns0SSPwBsOanGSz+eNHrb67+eQYSfO0bHcLaMiIharkYFZampqRg0aJDh+759+0Iul+Pq1asICwtr9IvPnz8fkydPRlxcHOLj4/Hpp58iJSUFM2bMAKCb5UpLS8Pq1ashlUoRExNT4/EBAQFQKpW1jlPzSki9jnKNFoFeCoT7Nr0huEYrsGhTIurLUFy0KREjooMgY9FYIiJqoRoVlGk0Gri6utZ8ArkclZWVZr34xIkTkZ2djVdeeQVqtRoxMTHYvHkzIiIiAABqtbrBmmVkfX8n6Zcu/SyyrHggOQfqvNI6bxcA1HmlOJCcg/h2fk1+PSIiInskEY3YQimVSjFmzJgay4GbNm3C0KFD4eHhYTi2ceNGy47SgvLz86FSqZCXlwcvLy9bD8chPfjZfvx5IRuvjo/B5Fsimvx8Pyak4cl1CQ3e771JPTGuZ5smvx4REZE1mRp7NGqmbOrUqbWOPfTQQ40fHTms8kotDl/OBWC5fLIAT6VF70dEROSIGhWUrVy5srnGQQ7i5NU8lFZo4evhig4BrSzynH2jfBGsUiI9r9RoXpkEQJBKadGdnkRERPbGrIr+5Lz0pTD6RPpYrEyFTCrBwrHRAHQBWHX67xeOjWaSPxERtWgMyqhRbtQns2zC/eiYYCx7qDeCVDWXKINUSix7qDfLYRARUYtnVpslck4arcBBCzYhv9nomGCMiA7CgeQcZBaUIsBTt2TJGTIiInIGDMrIZGfS81FQWolWCjm6BDfPzlWZVMKyF0RE5JS4fEkm0y9dxkX6cPaKiIjIwjhT5mQ0WtHo5UH9Y74/mgZAF5QRERGRZTEocyJbTqqxaFNijer5wSolFo6NrjOR3thjVu69hPatWzH5noiIyIK4fOkktpxUY+aaI7XaGaXnlWLmmiPYclJt8mNyisrrfAwRERGZhzNlTqC+ht/6Yy/+cBK+Hrr2WZVaLSoqtViw8USdj5GATcKJiIgsiUGZE2io4TcAZBWWY8Inf5n8nGwSTkREZFkMypxAZkH9AZmen4crVG4ukEklKCqrxNUGArnGPDcRERHVj0GZEzC1kfeHD/Q2zHr9dTEb9y/fb7HnJiIiovox0d8JnFbn1Xu7BLpdmNUbfuubhNeVLWbsMURERGQ+BmUtmBACb2w5g1d+Pm04ZmrDbzYJJyIisi4GZS1UhUaLpzccw7KdFwEAz4zsiGUPNq7hN5uEExERWY9ECGGs6kGLlZ+fD5VKhby8PHh5NU//RlsrKqvEzLVHsPvcNcikEiy+uxsmxIUBaFpFfzYJJyIiajxTYw8m+rcw1wrK8MiqgziRlgc3FxmWPtgbQzoHGG43p+E3m4QTERE1PwZlDuzmGawATwUe+eIgLmcXw9fDFZ9P64OeYd62HiYRERGZgEGZHTBnedBYT0qpBNAKIMzXDasf6Ycof4/mHjoRERFZCIMyGzO3SfjMNUdqtUDSVh2YPaQDAzIiIiIHw92XNmROk/D6+lgCunIV7+44B43WqfZvEBEROTzOlNlIQ03CJQAW/nQKwSo3qPNKcSW3GGnXS3D8Sl69fSzZk5KIiMgxMSizkYaahAsAGfllGPfRn2Y9P3tSEhERORYGZTZiatDkqZChbYAnQn3cEOrjhopKLT7/81KDj2NPSiIiIsfCoMxGTA2aPp3Sp8YypEYr8OvJdKTnlRpd+pRAV3GfPSmJiIgcCxP9bcTcht/sSUlERNQyMSizkerB1c0aCq7Yk5KIiKjlYe9LG/spIQ1PrkuosRTZUJ0yPfakJCIisn/sfekg3F3lEAD8PFzw0p1dEeBlenDFnpREREQtB4MyG9tyKh0AMLZHG4zr1cbGoyEiIiJbYU6ZDVVotNhxOgMAMDomyMajISIiIltiUGZDB5JzcL24An4erugTyRIWREREzoxBmQ1trVq6HBEdyAR9IiIiJ2fzoGzp0qWIioqCUqlEbGws9uzZU+d9N27ciBEjRqB169bw8vJCfHw8tm7dasXRWo5WKwxB2aiuXLokIiJydjYNytavX4958+bhhRdewNGjRzFo0CCMGTMGKSkpRu+/e/dujBgxAps3b8bhw4cxZMgQjB07FkePHrXyyJsu4cp1ZOSXoZVCjv7tuYOSiIjI2dm0Tlm/fv3Qu3dvLFu2zHCsS5cuGD9+PBYvXmzSc3Tt2hUTJ07ESy+9ZNL97aVO2eLNp/HJ7iTc1SME79/fy2bjICIiouZlauxhs5my8vJyHD58GCNHjqxxfOTIkdi3b59Jz6HValFQUABf37qT5MvKypCfn1/jy9aEEIZSGNx1SURERIANg7KsrCxoNBoEBgbWOB4YGIj09HSTnuN///sfioqKMGHChDrvs3jxYqhUKsNXWFhYk8ZtCWczCnA5uxgKuRSDO7a29XCIiIjIDtg80V8iqbnrUAhR65gxX3/9NV5++WWsX78eAQEBdd5vwYIFyMvLM3ylpqY2ecxNteWkLugc1KE1PBSs30tEREQ2rOjv7+8PmUxWa1YsMzOz1uzZzdavX4/p06djw4YNGD58eL33VSgUUCgUTR6vJemDMi5dEhERkZ7NZspcXV0RGxuL7du31zi+fft29O/fv87Hff3115g2bRq++uor3HHHHc09TIu7nF2EM+kFkEklGN6l7hk+IiIici42XTubP38+Jk+ejLi4OMTHx+PTTz9FSkoKZsyYAUC39JiWlobVq1cD0AVkU6ZMwXvvvYdbbrnFMMvm5uYGlUpls5+jMfS1yeLb+sHb3dXGoyEiIiJ7YdOgbOLEicjOzsYrr7wCtVqNmJgYbN68GREREQAAtVpdo2bZJ598gsrKSjzxxBN44oknDMenTp2KVatWWXv4ZtEvXY7qWv8SLRERETkXm9YpswVb1inLyC9Fv//+BgD4+/lhCPRSWvX1iYiIyPrsvk6ZM9pWtXTZO9ybARkRERHVwKDMilgwloiIiOrCoMxKrheXY39SDgA2ICciIqLaGJRZyY7TmdBoBToHeSLCz8PWwyEiIiI7w6DMSlgwloiIiOrDoMwKisoqsfv8NQAMyoiIiMg4BmVWsOvcNZRXahHp545OgZ62Hg4RERHZIQZlVnCjYGyQSc3WiYiIyPkwKGtmZZUa/H4mEwAwikuXREREVAcGZc1s34VsFJZVItBLgZ6h3rYeDhEREdkpBmXNrPrSpVTKpUsiIiIyjkFZM9JoBbafzgDAgrFERERUPwZlzejgpRzkFJXD290FfaN8bT0cIiIismMMypqRfulyeJdAuMh4qomIiKhujBSaiRAC2/QNyLl0SURERA1gUNZMTqTl4WpeKdxdZRjYwd/WwyEiIiI7J7f1AFoajVbgQHIOPt+bBAC4rWNrKF1kNh4VERER2TsGZRa05aQaizYlQp1Xaji272I2tpxUY3RMsA1HRkRERPaOy5cWsuWkGjPXHKkRkAFAXkkFZq45gi0n1TYaGRERETkCBmUWoNEKLNqUCGHkNv2xRZsSodEauwcRERERgzKLOJCcU2uGrDoBQJ1XigPJOdYbFBERETkUBmUWkFlQd0Bmzv2IiIjI+TAos4AAT6VF70dERETOh0GZBfSN8kWwSom62o1LAASrlGy1RERERHViUGYBMqkEC8dGA0CtwEz//cKx0ZBJ6wrbiIiIyNkxKLOQ0THBWPZQbwSpai5RBqmUWPZQb9YpIyIionqxeKwFjY4JxojoIBxIzkFmQSkCPHVLlpwhIyIiooYwKLMwmVSC+HZ+th4GERERORguXxIRERHZAQZlRERERHaAQRkRERGRHXC6nDIhdP0n8/PzbTwSIiIicgb6mEMfg9TF6YKygoICAEBYWJiNR0JERETOpKCgACqVqs7bJaKhsK2F0Wq1uHr1Kjw9PSGRNE+pivz8fISFhSE1NRVeXl7N8hqOgudCh+fhBp6LG3gudHgebuC5uKElnQshBAoKChASEgKptO7MMaebKZNKpQgNDbXKa3l5eTn8hWQpPBc6PA838FzcwHOhw/NwA8/FDS3lXNQ3Q6bHRH8iIiIiO8CgjIiIiMgOMChrBgqFAgsXLoRCobD1UGyO50KH5+EGnosbeC50eB5u4Lm4wRnPhdMl+hMRERHZI86UEREREdkBBmVEREREdoBBGREREZEdYFBGREREZAcYlFnY0qVLERUVBaVSidjYWOzZs8fWQ7K6l19+GRKJpMZXUFCQrYdlFbt378bYsWMREhICiUSCH374ocbtQgi8/PLLCAkJgZubG2677TacOnXKNoNtZg2di2nTptW6Tm655RbbDLYZLV68GH369IGnpycCAgIwfvx4nD17tsZ9nOG6MOU8OMs1sWzZMnTv3t1QFDU+Ph6//vqr4XZnuB70GjoXznJN6DEos6D169dj3rx5eOGFF3D06FEMGjQIY8aMQUpKiq2HZnVdu3aFWq02fJ04ccLWQ7KKoqIi9OjRAx9++KHR2998800sWbIEH374IQ4ePIigoCCMGDHC0JO1JWnoXADA6NGja1wnmzdvtuIIrWPXrl144oknsH//fmzfvh2VlZUYOXIkioqKDPdxhuvClPMAOMc1ERoaitdffx2HDh3CoUOHMHToUIwbN84QeDnD9aDX0LkAnOOaMBBkMX379hUzZsyocaxz587i3//+t41GZBsLFy4UPXr0sPUwbA6A+P777w3fa7VaERQUJF5//XXDsdLSUqFSqcTHH39sgxFaz83nQgghpk6dKsaNG2eT8dhSZmamACB27dolhHDe6+Lm8yCE814TQgjh4+MjPvvsM6e9HqrTnwshnO+a4EyZhZSXl+Pw4cMYOXJkjeMjR47Evn37bDQq2zl//jxCQkIQFRWFSZMmISkpydZDsrnk5GSkp6fXuEYUCgUGDx7slNcIAOzcuRMBAQHo2LEjHnvsMWRmZtp6SM0uLy8PAODr6wvAea+Lm8+DnrNdExqNBuvWrUNRURHi4+Od9noAap8LPWe6JpyuIXlzycrKgkajQWBgYI3jgYGBSE9Pt9GobKNfv35YvXo1OnbsiIyMDLz22mvo378/Tp06BT8/P1sPz2b014Gxa+Ty5cu2GJJNjRkzBvfddx8iIiKQnJyM//znPxg6dCgOHz7cYit4CyEwf/58DBw4EDExMQCc87owdh4A57omTpw4gfj4eJSWlqJVq1b4/vvvER0dbQi8nOl6qOtcAM51TQAMyixOIpHU+F4IUetYSzdmzBjDv7t164b4+Hi0a9cOX3zxBebPn2/DkdkHXiM6EydONPw7JiYGcXFxiIiIwC+//IK7777bhiNrPrNnz8bx48exd+/eWrc503VR13lwpmuiU6dOSEhIwPXr1/Hdd99h6tSp2LVrl+F2Z7oe6joX0dHRTnVNAEz0txh/f3/IZLJas2KZmZm1PvE4Gw8PD3Tr1g3nz5+39VBsSr8DldeIccHBwYiIiGix18mcOXPw008/4Y8//kBoaKjhuLNdF3WdB2Na8jXh6uqK9u3bIy4uDosXL0aPHj3w3nvvOd31ANR9LoxpydcEwKDMYlxdXREbG4vt27fXOL59+3b079/fRqOyD2VlZTh9+jSCg4NtPRSbioqKQlBQUI1rpLy8HLt27XL6awQAsrOzkZqa2uKuEyEEZs+ejY0bN+L3339HVFRUjdud5bpo6DwY01KvCWOEECgrK3Oa66E++nNhTIu/Jmy1w6AlWrdunXBxcRErVqwQiYmJYt68ecLDw0NcunTJ1kOzqqefflrs3LlTJCUlif3794s777xTeHp6OsV5KCgoEEePHhVHjx4VAMSSJUvE0aNHxeXLl4UQQrz++utCpVKJjRs3ihMnToj7779fBAcHi/z8fBuP3PLqOxcFBQXi6aefFvv27RPJycnijz/+EPHx8aJNmzYt7lzMnDlTqFQqsXPnTqFWqw1fxcXFhvs4w3XR0HlwpmtiwYIFYvfu3SI5OVkcP35cPP/880IqlYpt27YJIZzjetCr71w40zWhx6DMwj766CMREREhXF1dRe/evWts93YWEydOFMHBwcLFxUWEhISIu+++W5w6dcrWw7KKP/74QwCo9TV16lQhhK78wcKFC0VQUJBQKBTi1ltvFSdOnLDtoJtJfeeiuLhYjBw5UrRu3Vq4uLiI8PBwMXXqVJGSkmLrYVucsXMAQKxcudJwH2e4Lho6D850TTzyyCOG94nWrVuLYcOGGQIyIZzjetCr71w40zWhJxFCCOvNyxERERGRMcwpIyIiIrIDDMqIiIiI7ACDMiIiIiI7wKCMiIiIyA4wKCMiIiKyAwzKiIiIiOwAgzIiIiIiO8CgjIiIiMgOMCgjIpNMmzYNEokEEokEcrkc4eHhmDlzJnJzc209NIc3bdo0jB8/3tbDICIbY1BGRCYbPXo01Go1Ll26hM8++wybNm3CrFmzbD0ssiAhBCorK209DCKnxKCMiEymUCgQFBSE0NBQjBw5EhMnTsS2bdtq3GflypXo0qULlEolOnfujKVLlxpuKy8vx+zZsxEcHAylUonIyEgsXrzYcLtEIsGyZcswZswYuLm5ISoqChs2bKjx/CdOnMDQoUPh5uYGPz8//POf/0RhYaHhdv2s09tvv43g4GD4+fnhiSeeQEVFheE+S5cuRYcOHaBUKhEYGIh7773XcJsQAm+++Sbatm0LNzc39OjRA99++22956WsrAz/+te/EBYWBoVCgQ4dOmDFihUAAI1Gg+nTpyMqKgpubm7o1KkT3nvvPcNjX375ZXzxxRf48ccfDTORO3fuBACkpaVh4sSJ8PHxgZ+fH8aNG4dLly4ZHltZWYm5c+fC29sbfn5+eO655zB16tQas25lZWWYO3cuAgICoFQqMXDgQBw8eNBw+86dOyGRSLB161bExcVBoVDgyy+/hFQqxaFDh2r8nB988AEiIiLA7nxEzcSmnTeJyGFMnTpVjBs3zvD9xYsXRXR0tAgMDDQc+/TTT0VwcLD47rvvRFJSkvjuu++Er6+vWLVqlRBCiLfeekuEhYWJ3bt3i0uXLok9e/aIr776yvB4AMLPz08sX75cnD17Vrz44otCJpOJxMREIYQQRUVFhib3J06cEL/99puIiooyNHzXj9PLy0vMmDFDnD59WmzatEm4u7uLTz/9VAghxMGDB4VMJhNfffWVuHTpkjhy5Ih47733DI9//vnnRefOncWWLVvExYsXxcqVK4VCoRA7d+6s89xMmDBBhIWFiY0bN4qLFy+KHTt2iHXr1gkhhCgvLxcvvfSSOHDggEhKShJr1qwR7u7uYv369UIIIQoKCsSECRPE6NGjhVqtFmq1WpSVlYmioiLRoUMH8cgjj4jjx4+LxMRE8cADD4hOnTqJsrIyIYQQr732mvD19RUbN24Up0+fFjNmzBBeXl41/j/NnTtXhISEiM2bN4tTp06JqVOnCh8fH5GdnS2EuNE4vnv37mLbtm3iwoULIisrS4wYMULMmjWrxs/Zq1cv8dJLL9V/oRCR2RiUEZFJpk6dKmQymfDw8BBKpVIAEADEkiVLDPcJCwurEWQJIcSrr74q4uPjhRBCzJkzRwwdOlRotVqjrwFAzJgxo8axfv36iZkzZwohdEGfj4+PKCwsNNz+yy+/CKlUKtLT0w3jjIiIEJWVlYb73HfffWLixIlCCCG+++474eXlJfLz82u9fmFhoVAqlWLfvn01jk+fPl3cf//9Rsd89uxZAUBs377d6O3GzJo1S9xzzz2G728OeIUQYsWKFaJTp041zlVZWZlwc3MTW7duFUIIERgYKN566y3D7ZWVlSI8PNzwXIWFhcLFxUWsXbvWcJ/y8nIREhIi3nzzTSHEjaDshx9+qPH669evFz4+PqK0tFQIIURCQoKQSCQiOTnZ5J+TiBqHy5dEZLIhQ4YgISEBf//9N+bMmYNRo0Zhzpw5AIBr164hNTUV06dPR6tWrQxfr732Gi5evAhAt7SYkJCATp06Ye7cubWWPgEgPj6+1venT58GAJw+fRo9evSAh4eH4fYBAwZAq9Xi7NmzhmNdu3aFTCYzfB8cHIzMzEwAwIgRIxAREYG2bdti8uTJWLt2LYqLiwEAiYmJKC0txYgRI2r8DKtXrzb8DDdLSEiATCbD4MGD6zxvH3/8MeLi4tC6dWu0atUKy5cvR0pKSt0nGsDhw4dx4cIFeHp6Gsbh6+uL0tJSXLx4EXl5ecjIyEDfvn0Nj5HJZIiNjTV8f/HiRVRUVGDAgAGGYy4uLujbt6/hnOrFxcXV+H78+PGQy+X4/vvvAQCff/45hgwZgsjIyHrHTUTmk9t6AETkODw8PNC+fXsAwPvvv48hQ4Zg0aJFePXVV6HVagEAy5cvR79+/Wo8Th8g9e7dG8nJyfj111+xY8cOTJgwAcOHD28wZ0sikQDQ5Xvp/13XfQBd4HHzbfrxeXp64siRI9i5cye2bduGl156CS+//DIOHjxouM8vv/yCNm3a1HgOhUJh9HXd3NzqHfs333yDp556Cv/73/8QHx8PT09PvPXWW/j777/rfZxWq0VsbCzWrl1b67bWrVvX+NmqE9XyvfT/Nnafm49VD3QBwNXVFZMnT8bKlStx991346uvvsK7775b75iJqGk4U0ZEZlu4cCHefvttXL16FYGBgWjTpg2SkpLQvn37Gl9RUVGGx3h5eWHixIlYvnw51q9fj++++w45OTmG2/fv31/jNfbv34/OnTsDAKKjo5GQkICioiLD7X/++SekUik6duxo8rjlcjmGDx+ON998E8ePH8elS5fw+++/Izo6GgqFAikpKbV+hrCwMKPP1a1bN2i1Wuzatcvo7Xv27EH//v0xa9Ys9OrVC+3bt6816+bq6gqNRlPjWO/evXH+/HkEBATUGotKpYJKpUJgYCAOHDhgeIxGo8HRo0cN37dv3x6urq7Yu3ev4VhFRQUOHTqELl26NHieHn30UezYsQNLly5FRUUF7r777gYfQ0Tm40wZEZnttttuQ9euXfHf//4XH374IV5++WXMnTsXXl5eGDNmDMrKynDo0CHk5uZi/vz5eOeddxAcHIyePXtCKpViw4YNCAoKgre3t+E5N2zYgLi4OAwcOBBr167FgQMHDDsZH3zwQSxcuBBTp07Fyy+/jGvXrmHOnDmYPHkyAgMDTRrzzz//jKSkJNx6663w8fHB5s2bodVq0alTJ3h6euKZZ57BU089Ba1Wi4EDByI/Px/79u1Dq1atMHXq1FrPFxkZialTp+KRRx7B+++/jx49euDy5cvIzMzEhAkT0L59e6xevRpbt25FVFQUvvzySxw8eLBGoBoZGYmtW7fi7Nmz8PPzg0qlwoMPPoi33noL48aNwyuvvILQ0FCkpKRg48aNePbZZxEaGoo5c+Zg8eLFaN++PTp37owPPvgAubm5hlkwDw8PzJw5E88++yx8fX0RHh6ON998E8XFxZg+fXqD56pLly645ZZb8Nxzz+GRRx5pcFaQiJrItiltROQojCWjCyHE2rVrhaurq0hJSTF837NnT+Hq6ip8fHzErbfeKjZu3CiE0CXq9+zZU3h4eAgvLy8xbNgwceTIEcNzARAfffSRGDFihFAoFCIiIkJ8/fXXNV7v+PHjYsiQIUKpVApfX1/x2GOPiYKCgnrH+eSTT4rBgwcLIYTYs2ePGDx4sPDx8RFubm6ie/fuhp2QQgih1WrFe++9Jzp16iRcXFxE69atxahRo8SuXbvqPDclJSXiqaeeEsHBwcLV1VW0b99efP7550IIIUpLS8W0adOESqUS3t7eYubMmeLf//636NGjh+HxmZmZYsSIEaJVq1YCgPjjjz+EEEKo1WoxZcoU4e/vLxQKhWjbtq147LHHRF5enhBCiIqKCjF79mzh5eUlfHx8xHPPPSfuu+8+MWnSpBpjmzNnjuE5BgwYIA4cOGC4XZ/on5uba/RnW7FihQBQ4zFE1DwkQrDgDBHZB4lEgu+//57V7c2k1WrRpUsXTJgwAa+++qpFnvP//u//sG7dOpw4ccIiz0dEdePyJRGRg7p8+TK2bduGwYMHo6ysDB9++CGSk5PxwAMPNPm5CwsLcfr0aXzwwQcWC/CIqH5M9CciclBSqRSrVq1Cnz59MGDAAJw4cQI7duwwKYm/IbNnz8bAgQMxePBgPPLIIxYYLRE1hMuXRERERHaAM2VEREREdoBBGREREZEdYFBGREREZAcYlBERERHZAQZlRERERHaAQRkRERGRHWBQRkRERGQHGJQRERER2QEGZURERER24P8B+DyHrcXReJAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "probs = expit_func(sequence_idata.posterior.YearsAtCompany_threshold).mean((\"chain\", \"draw\"))\n", + "probs = np.append(probs, 1)\n", + "\n", + "plt.figure(figsize=(7, 3))\n", + "plt.plot(sorted(attrition.YearsAtCompany.unique()), probs, marker='o')\n", + "plt.ylabel(\"Probability\")\n", + "plt.xlabel(\"Response category\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This plot can seem confusing at first. Remember, the sequential model is a product of probabilities, i.e., the probability that $Y$ is equal to category $k$ is equal to the probability that it did not fall in one of the former categories $1: k-1$ multiplied by the probability that the sequential process stopped at $k$. Thus, the probability of category 5 is the probability that the sequential process did not fall in 0, 1, 2, 3, or 4 multiplied by the probability that the sequential process stopped at 5. This makes sense why the probability of category 36 is 1. There is no category after 36, so once you multiply all of the previous probabilities with the current category, you get 1. This is the reason for the \"cumulative-like\" shape of the plot. But if the coefficients were truly cumulative, the probability could not decreases as $k$ increases. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Posterior predictive samples\n", + "\n", + "Again, using the posterior predictive samples, we can visualize the model fit against the observed data. In the case of the sequential model, the model does an alright job of capturing the observed frequencies of the categories. For pedagogical purposes, this fit is sufficient." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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MmAqbjOHr6wsHBweNkbU9e/bgzp07GqelK7pvFySEKLVMy5Ytcfr0aYwZMwZ79uxBWlpaubfTtGlT1KtXr8zli/sezv8cX5QjR44gIyOjUF93cXFB586dC/V1mUyGnj17aixr2rSp1t/X/3U8zUrF8vT0hKenJ4CnQ95Tp07FokWLEBoaitDQUNy9excA8NprrxVZP/8UR0pKCoCnPyoFOTg4aDV1PX/bkydPxuTJk4ssU/DH2traWuN9/inHjIyMcm8fACZOnIhvvvkGU6dOhbe3NywtLaGnp4cRI0Zo3SYAWFlZabxXKBQlLn/y5AlMTU2RkpICAwODQqc5ZDIZHBwcpM+huM/DwMCg0DG6e/cutm/fXuxp9vIkRADQpUsXjR/JgICAUm/ZYmRkhL/++gvA08/M1dVVY5ZeSkpKkTNPnZycpPUA4O/vj5ycHKxatQpvvfUW8vLy8Nprr+Gzzz5Dt27dSowhv7+9/fbbxZa5f/++ximussziy4+tqLJOTk6Ffri0mRkYEREBlUoFuVwOZ2fnQp9xce3evXsXV65cKfWzz+93Bdst6u+9oOTkZACo0EkhBgYG8Pf3x9KlS/HgwQNYWFhgzZo1cHR0hI+Pj1Suovt2QfmfXX4/LMq0adNgYmKC9evXY8WKFdDX10eHDh3w5ZdfSt+9pSlvnyjue/j06dPlaqe8Suvr4eHhGsuMjY01/sEBPP37fxG3JPovYDJHZSKXyxEcHIxFixZJ17LkX8vzyy+/FHtNCPC/JCoxMbHQuoLL8v94C17kWvCLNX/b06ZNQ9++fYvcrrbXrJVV/jVUc+fO1Vh+7969ct1CoKJYW1sjJycHycnJGgmdEAKJiYlS0v3s5/HsTNCcnBzpCzefjY0NmjZtis8//7zIbZb0Q1WUlStXalyX9+z1YMXR09Mr8YfN2toaCQkJhZbnT255dhtDhw7F0KFDkZ6ejr/++gvBwcHw8/PDv//+W2Ifzm9j6dKlxc5qtre313hflpGm/M8iISGhUEJz586dQsdHm9GrZs2alXqci2rXxsYGRkZG+OGHH4qsk99mfr9LSUnRSOiK+nsvKL+fFpzs8byGDh2K+fPnS9fQ/vHHHxg/fjz09fU14q/Ivl3QH3/8AZlMhg4dOhRbxsDAABMnTsTEiRPx4MEDREREYPr06fDx8cHNmzfLNKO4vH2iuO/hZz87Q0NDqNXqQuWeJ8F9tq8XVFRfp/JhMkeFJCQkFPmvp/xTh/lfcj4+PjAwMMDVq1dLPKXk7u4OR0dH/PTTT5g4caL05XP9+nVERkZqfGnmj7CcOXNG41/Rf/zxR6E269ati9OnTxdKpp6HUqks86ha/k1sn7Vz507cvn0bderUqbCYyqpLly4IDQ3F+vXrMWHCBGn5r7/+ivT0dOkC/fxZahs2bECLFi2kcj///HOhWXx+fn7YtWsXXnnlFVhaWj53jC8iwe7SpQvmzZuHkydPSpMWAODHH3+ETCZDp06dCtUxMTGBr68vsrKy0KdPH5w/fx6urq7Fjta2bdsWFhYWuHDhAsaOHVthseef2ly/fr3GCPfx48cRGxuLGTNmVNi2ysvPzw9z586FtbU13Nzcii3XqVMnhIaGYsOGDQgKCpKWb9y4sdRttGnTBiqVCitWrMC7775bbGLy7OdiZGRUarsNGjRAq1atEBYWhtzcXGRmZmLo0KGF9q8i+/azwsLC8Oeff2LgwIGoWbNmmepYWFjg7bffxu3btzF+/HjEx8ejYcOGz30GoaDivoeHDBkilalVqxa2bNmCzMxMafspKSmIjIzUGBUvT2xeXl4wMjLC+vXrpVnjwNNEfv/+/SWOelPpmMxRIT4+PnB2dkbPnj1Rv3595OXlISYmBgsWLICpqSk+/PBDAE//4D/99FPMmDED165dQ48ePWBpaYm7d+/i2LFjMDExkWaUzZkzByNGjMCbb76JkSNH4sGDBwgJCSk05O/g4ICuXbti3rx5sLS0hKurK/bt24etW7cWinPlypXw9fWFj48PAgMDUaNGDdy/fx+xsbE4efIktmzZUu59b9KkCTZt2oTNmzejdu3aMDQ0RJMmTYos6+fnhzVr1qB+/fpo2rQpoqOjMX/+/Ao9ZVQe3bp1g4+PD6ZOnYq0tDS0bdtWms3q4eEBf39/AE9/6AYPHozFixdDLpeja9euOHfuHL766qtCNxn99NNPER4ejjZt2iAoKAju7u548uQJ4uPjsWvXLqxYsaLS9jffhAkT8OOPP+KNN97Ap59+CldXV+zcuRPLly/H6NGjpeuJRo4cCSMjI7Rt2xaOjo5ITEzEvHnzoFKppESqcePGAIDvvvsOZmZmMDQ0hJubG6ytrbF06VIEBATg/v37ePvtt2FnZ4fk5GScPn0aycnJ+Pbbb8sdu7u7O0aNGoWlS5dCT09PuofezJkz4eLiopGUv2zjx4/Hr7/+ig4dOmDChAlo2rQp8vLycOPGDezduxeTJk1Cq1at0L17d3To0AFTpkxBeno6PD098c8//2DdunWlbsPU1BQLFizAiBEj0LVrV4wcORL29va4cuUKTp8+jWXLlgGA9Df45ZdfwtfXF/r6+mjatKl0qUFRhg0bhvfeew937txBmzZtCv1DoiL6dkZGBqKioqT/v3btGn777Tfs2LFDmp1bkp49e6Jx48bw9PSEra0trl+/jsWLF8PV1VWa2Zm/70uWLEFAQADkcjnc3d1hZmZWYtvFSUpKkr6H1Wo1goODYWhoiGnTpkll/P39sXLlSgwePBgjR45ESkoKQkNDC30/mJmZwdXVFb///ju6dOkCKysr2NjYFHnZg4WFBWbOnInp06djyJAhGDBgAFJSUjB79mwYGhoiODhYq/2h/1fJEzCoCtq8ebMYOHCgqFu3rjA1NRVyuVzUrFlT+Pv7iwsXLhQq/9tvv4lOnToJc3NzoVQqhaurq3j77bdFRESERrnvv/9e1K1bVygUClGvXj3xww8/FJodJYQQCQkJ4u233xZWVlZCpVKJwYMHixMnThQ5I/T06dOiX79+ws7OTsjlcuHg4CA6d+4sVqxYIZXJn1VVcFZeUTNn4+PjRffu3YWZmZkAIMVW1IzU1NRUMXz4cGFnZyeMjY1Fu3btxOHDhwvNBCvvbNYtW7ZoLC8u/vzZisnJydKyjIwMMXXqVOHq6irkcrlwdHQUo0ePFqmpqRp1MzMzxaRJk4SdnZ0wNDQUrVu3FkeOHCk0A06IpzPWgoKChJubm5DL5cLKykq0aNFCzJgxQzx69Egqhxc4m7U0169fFwMHDhTW1tZCLpcLd3d3MX/+fGlGsxBCrF27VnTq1EnY29sLhUIhnJycRL9+/cSZM2c02lq8eLFwc3MT+vr6hT63Q4cOiTfeeENYWVkJuVwuatSoId544w2Nz6yoz6Xgumfl5uaKL7/8UtSrV0/I5XJhY2MjBg8eLG7evKlRrrTZk8Vtq6g4ytruo0ePxCeffCLc3d2FQqEQKpVKNGnSREyYMEEkJiZK5R48eCCGDRsmLCwshLGxsejWrZu4ePFiqbNZ8+3atUt4e3sLExMTYWxsLBo2bKgxCzQzM1OMGDFC2NraCplMptFGUX1WCCHUarUwMjISAMSqVauK3L+y9u3ijhv+f8Y/AGFiYiJq164t3n77bbFlyxaNvpevYKwLFiwQbdq0ETY2NkKhUIiaNWuK4cOHi/j4eI1606ZNE05OTkJPT0/jO8vV1VW88cYbRcZX3GzWdevWiaCgIGFrayuUSqVo3769OHHiRKH6a9euFQ0aNBCGhoaiYcOGYvPmzUV+X0dERAgPDw+hVCo1ZsMX91l///33omnTplJ/6t27d6GZw8X93Rf190NPyYQow5QbohckMDAQBw8e5PP7iIiItMRbkxARERHpMCZzRERERDqMp1mJiIiIdBhH5oiIiIh0GJM5IiIiIh3GZI6IiIhIh1XqTYPnzZuHrVu34uLFizAyMkKbNm3w5ZdfatzcMTAwEGvXrtWo16pVK+lGjcDTRz9NnjwZP/30EzIyMtClSxcsX75c44aPqampCAoKkp4k0KtXLyxdurTMj13Ky8vDnTt3YGZmVmEPhCYiIiIqihACDx8+hJOTk/Ss85IKVxofHx8RFhYmzp07J2JiYsQbb7whatasqXGzxoCAANGjRw+RkJAgvVJSUjTaef/990WNGjVEeHi4OHnypOjUqZNo1qyZyMnJkcr06NFDNG7cWERGRorIyEjRuHFj4efnV+ZYb968qXGDSL744osvvvjii68X/Sp4A/GiVKnZrMnJybCzs8OhQ4ekhxMHBgbiwYMH+O2334qso1arYWtri3Xr1qF///4Anj6018XFBbt27YKPjw9iY2PRsGFDREVFoVWrVgCAqKgoeHl54eLFi2V6XqRarYaFhQVu3rxZ6JEmRERERBUpLS0NLi4uePDgAVQqVYllq9SzWdVqNQDAyspKY/nBgwdhZ2cHCwsLeHt74/PPP4ednR0AIDo6GtnZ2ejevbtU3snJCY0bN0ZkZCR8fHxw5MgRqFQqKZEDgNatW0OlUiEyMrLIZC4zMxOZmZnS+4cPHwIAzM3NmcwRERHRS1GWS7uqzAQIIQQmTpyIdu3aSQ+7BgBfX19s2LAB+/fvx4IFC3D8+HF07txZSrQSExOhUChgaWmp0Z69vT0SExOlMvnJ37Ps7OykMgXlP4A7/+Xi4lJRu0pERERUYarMyNzYsWNx5swZ/P333xrL80+dAkDjxo3h6ekJV1dX7Ny5E3379i22PSGERjZbVGZbsMyzpk2bhokTJ0rv84c7iYiIiKqSKjEyN27cOPzxxx84cOCAxgzUojg6OsLV1RWXL18GADg4OCArKwupqaka5ZKSkmBvby+VuXv3bqG2kpOTpTIFKZVK6ZQqT60SERFRVVWpI3NCCIwbNw7btm3DwYMH4ebmVmqdlJQU3Lx5E46OjgCAFi1aQC6XIzw8HP369QMAJCQk4Ny5cwgNDQUAeHl5Qa1W49ixY2jZsiUA4OjRo1Cr1WjTps0L2jsiopcjNzcX2dnZlR0GEZWTXC6Hvr7+c7dTqbNZx4wZg40bN+L333/XmISgUqlgZGSER48eISQkBG+99RYcHR0RHx+P6dOn48aNG4iNjYWZmRkAYPTo0dixYwfWrFkDKysrTJ48GSkpKYiOjpYOkq+vL+7cuYOVK1cCAEaNGgVXV1ds3769TLGmpaVBpVJBrVZzlI6IqoxHjx7h1q1bqEI3JiCiMpLJZHB2doapqWmhdeXJOyo1mSvuerWwsDAEBgYiIyMDffr0walTp/DgwQM4OjqiU6dOmDNnjsb1a0+ePMFHH32EjRs3atw0+Nky9+/fL3TT4GXLlpX5psFM5oioqsnNzcXly5dhbGwMW1tb3tCcSIcIIZCcnIzHjx+jbt26hUbodCaZ0yVM5oioqnny5Ani4uJQq1YtGBkZVXY4RFROGRkZiI+Ph5ubGwwNDTXWlSfvqDKzWUn3pGbkIju3/P8WkOvLYGn0/NcIENFTHJEj0k0V9bfLZI60kpqRix9jHmhdf8irFkzoiIiIKkCVuDUJ6R5tRuQqsj4RERE9xWSOtJaVK/AkJ6/crywmckRURrVq1cLixYsrO4wKo83+BAYGok+fPi8kHvpvYDJHWkl5nIMb6mzcSssp9+uGOhspj3MqexeIqBLdvHkTw4cPh5OTExQKBVxdXfHhhx8iJSWlskPTefHx8ZDJZIiJiansUOglYTJHWsnKFcjKLf+o3NOROY7OEVVn165dg6enJ/7991/89NNPuHLlClasWIF9+/bBy8sL9+/fr7TYcnNzkZeXV2nbJ9IGkznSyr30HFxJycRVLV5XUjJxL50jc0TV1QcffACFQoG9e/fC29sbNWvWhK+vLyIiInD79m3MmDFDo/zDhw8xcOBAmJqawsnJCUuXLtVYHxISgpo1a0KpVMLJyQlBQUHSuqysLEyZMgU1atSAiYkJWrVqhYMHD0rr16xZAwsLC+zYsQMNGzaEUqnEqlWrYGhoiAcPHmhsJygoCN7e3tL7yMhIdOjQAUZGRnBxcUFQUBDS09Ol9UlJSejZsyeMjIzg5uaGDRs2lHpscnNzMXHiRFhYWMDa2hpTpkwpdEPo3bt3o127dlIZPz8/XL16VVqf/zQlDw8PyGQydOzYEQBw/PhxdOvWDTY2NlCpVPD29sbJkydLjYmqPs5mJa3cSsvGTbX2jw+6lcZHDxG9CJ6enkhMTHzp23VwcMCJEydKLXf//n3s2bMHn3/+eaF74zk4OGDQoEHYvHkzli9fLt22Yf78+Zg+fTpCQkKwZ88eTJgwAfXr10e3bt3wyy+/YNGiRdi0aRMaNWqExMREnD59Wmpz6NChiI+Px6ZNm+Dk5IRt27ahR48eOHv2LOrWrQsAePz4MebNm4fvv/8e1tbWcHZ2RnBwMH799VcMHz4cwNMk6+eff8ann34KADh79ix8fHwwZ84crF69GsnJyRg7dizGjh2LsLAwAE+vdbt58yb2798PhUKBoKAgJCUllXh8FixYgB9++AGrV69Gw4YNsWDBAmzbtg2dO3eWyqSnp2PixIlo0qQJ0tPTMWvWLLz55puIiYmBnp6e9OjKiIgINGrUCAqFAsDTpDggIABff/21tK3XX38dly9flp6oRDpKUJmo1WoBQKjV6soOpUqwq91Q6JtaCX0TLV6mVsKudsPK3gUinZeRkSEuXLggMjIypGU1atQQAF76q0aNGmWKOSoqSgAQ27ZtK3L9woULBQBx9+5dIYQQrq6uokePHhpl+vfvL3x9fYUQQixYsEDUq1dPZGVlFWrrypUrQiaTidu3b2ss79Kli5g2bZoQQoiwsDABQMTExGiUCQoKEp07d5be79mzRygUCnH//n0hhBD+/v5i1KhRGnUOHz4s9PT0REZGhrh06ZIAIKKioqT1sbGxAoBYtGhRcYdHODo6ii+++EJ6n52dLZydnUXv3r2LrZOUlCQAiLNnzwohhIiLixMAxKlTp4qtI4QQOTk5wszMTGzfvr3EcvTiFPU3nK88eQdH5kgrqcmJyH2k/XUtqbzHKdEL4eDgoNPbFf9/SvHZm6l6eXlplPHy8pJmhL7zzjtYvHgxateujR49euD1119Hz549YWBggJMnT0IIgXr16mnUz8zMhLW1tfReoVCgadOmGmUGDRoELy8v3LlzB05OTtiwYQNef/11WFpaAgCio6Nx5coVjVOnQgjk5eUhLi4O//77LwwMDODp6Smtr1+/fomPkFSr1UhISNDY3/w2xDOnWq9evYqZM2ciKioK9+7dk67xu3HjBho3blxs+0lJSZg1axb279+Pu3fvIjc3F48fP8aNGzeKrUO6gckcaUX6XpHJoG9sWeZ6uY9TASHAh8gRvRhlOdVZmerUqQOZTIYLFy4UebuNixcvwtLSEjY2NiW2k5/subi44NKlSwgPD0dERATGjBmD+fPn49ChQ8jLy4O+vj6io6MLPffy2QebGxkZFboTf8uWLfHKK69g06ZNGD16NLZt2yadPgWAvLw8vPfeexrX5+WrWbMmLl26pBFnRerZsydcXFywatUqODk5IS8vD40bN0ZWVlaJ9QIDA5GcnIzFixfD1dUVSqUSXl5epdajqo/JHD0XfWNL1P4ssszlr33SBrnplTdTjYgql7W1Nbp164bly5djwoQJGtfNJSYmYsOGDRgyZIhGEhQVFaXRRlRUFOrXry+9NzIyQq9evdCrVy988MEHqF+/Ps6ePQsPDw/k5uYiKSkJ7du3L3esAwcOxIYNG+Ds7Aw9PT288cYb0rrmzZvj/PnzqFOnTpF1GzRogJycHJw4cQItW7YEAFy6dKnQpIpnqVQqODo6IioqCh06dAAA5OTkIDo6Gs2bNwcApKSkIDY2FitXrpT26e+//9ZoJ/8audzcXI3lhw8fxvLly/H6668DeHp7mHv37pX1cFAVxtmsRET0Ui1btgyZmZnw8fHBX3/9hZs3b2L37t3o1q0batSogc8//1yj/D///IPQ0FD8+++/+Oabb7BlyxZ8+OGHAJ7ORl29ejXOnTuHa9euYd26dTAyMoKrqyvq1auHQYMGYciQIdi6dSvi4uJw/PhxfPnll9i1a1epcQ4aNAgnT57E559/jrffflvjQehTp07FkSNH8MEHHyAmJgaXL1/GH3/8gXHjxgEA3N3d0aNHD4wcORJHjx5FdHQ0RowYUWjSR0EffvghvvjiC2zbtg0XL17EmDFjNBJAS0tLWFtb47vvvsOVK1ewf/9+TJw4UaMNOzs7GBkZYffu3bh79y7UajWAp6Oi69atQ2xsLI4ePYpBgwaVGg/pBiZz9HxkgLO5vMwv8Fo5omqvbt26OHHiBF555RX0798fr7zyCkaNGoVOnTrhyJEjsLKy0ig/adIkREdHw8PDA3PmzMGCBQvg4+MDALCwsMCqVavQtm1bNG3aFPv27cP27dula+LCwsIwZMgQTJo0Ce7u7ujVqxeOHj0KFxeXMsX52muv4cyZMxg0aJDGuqZNm+LQoUO4fPky2rdvDw8PD8ycOROOjo5SmbCwMLi4uMDb2xt9+/bFqFGjYGdnV+I2J02ahCFDhiAwMBBeXl4wMzPDm2++Ka3X09PDpk2bEB0djcaNG2PChAmYP3++RhsGBgb4+uuvsXLlSjg5OaF3794AgB9++AGpqanw8PCAv78/goKCSo2HdINMCF69VBZpaWlQqVRQq9UwNzev7HAqncLcGtkP70Pf1ApD158pc72wwU2R++g+5GZWyErjnd6JnseTJ08QFxcHNzc3jVEjItINJf0Nlyfv4DVz9NwMDTjAS0REVFmYzNFzkQFwNi97N+JZViIioorFZI6eG0fmiIiIKg9/hYmIiIh0GJM5IiIiIh3G06z0XGQyYEATVZnLf8SL5oiIiCoUkzl6bnam7EZERESVhadZiYiIiHQYkzkiIiIiHcZkjoiIqBhr1qyBhYVFZYdRIWQyGX777TcAQHx8PGQyGWJiYrRuryLaqEoK7s/Bgwchk8k0no1bVTGZIyKilyowMBAymQwymQxyuRy1a9fG5MmTkZ6e/txtV3SC0b9/f/z7778V0lZV4uLigoSEBDRu3LhM5QMDA9GnT5/nakPXtGnTBgkJCVCpyj7Jr7LwynUiInrpevTogbCwMGRnZ+Pw4cMYMWIE0tPT8e2331Z2aJLs7GwYGRnByMjouduRy+UVFlNFtKWvrw8HB4dKb6MqUygUOrN/HJkjIqKXTqlUwsHBAS4uLhg4cCAGDRoknQLMzMxEUFAQ7OzsYGhoiHbt2uH48eNS3dTUVAwaNAi2trYwMjJC3bp1ERYWBgBwc3MDAHh4eEAmk6Fjx45SvbCwMDRo0ACGhoaoX78+li9fLq3LH9H7+eef0bFjRxgaGmL9+vVFnmb99ttv8corr0ChUMDd3R3r1q3TWC+TybBixQr07t0bJiYm+Oyzz4o8BrVq1cKcOXMwcOBAmJqawsnJCUuXLi1TW9u3b0eLFi1gaGiI2rVrY/bs2cjJyZHqXb58GR06dIChoSEaNmyI8PBwjXaLGsE8f/483njjDZibm8PMzAzt27fH1atXERISgrVr1+L333+XRlQPHjyo0UZeXh6cnZ2xYsUKje2cPHkSMpkM165dAwCo1WqMGjUKdnZ2MDc3R+fOnXH69Okij0/Bz6V9+/YwMjLCa6+9hn///RfHjx+Hp6cnTE1N0aNHDyQnJ2vULenzBoBjx47Bw8MDhoaG8PT0xKlTpzTWFzzNmpKSggEDBsDZ2RnGxsZo0qQJfvrpJ406HTt2RFBQEKZMmQIrKys4ODggJCSk2P2rKByZIyL6D3n9y51IfvjkpW/X1swQu6a+oXV9IyMjZGdnAwCmTJmCX3/9FWvXroWrqytCQ0Ph4+ODK1euwMrKCjNnzsSFCxfw559/wsbGBleuXEFGRgaApz/QLVu2REREBBo1agSFQgEAWLVqFYKDg7Fs2TJ4eHjg1KlTGDlyJExMTBAQECDFMXXqVCxYsABhYWFQKpXYu3evRpzbtm3Dhx9+iMWLF6Nr167YsWMHhg4dCmdnZ3Tq1EkqFxwcjHnz5mHRokXQ19cvdr/nz5+P6dOnIyQkBHv27MGECRNQv359dOvWrdi29uzZg8GDB+Prr7+WEq5Ro0ZJZfPy8tC3b1/Y2NggKioKaWlpGD9+fInH//bt2+jQoQM6duyI/fv3w9zcHP/88w9ycnIwefJkxMbGIi0tTUqarayscOfOHam+np4e3n33XWzYsAHvv/++tHzjxo3w8vJC7dq1IYTAG2+8ASsrK+zatQsqlQorV65Ely5d8O+//8LKyqrY+IKDg7F48WLUrFkTw4YNw4ABA2Bubo4lS5bA2NgY/fr1w6xZs6SR3dI+7/T0dPj5+aFz585Yv3494uLi8OGHH5Z4jJ48eYIWLVpg6tSpMDc3x86dO+Hv74/atWujVatWUrm1a9di4sSJOHr0KI4cOYLAwEC0bdtW4zOtaEzmiIj+Q5IfPkHig8eVHUa5HDt2DBs3bkSXLl2kU61r1qyBr68vgKc/zOHh4Vi9ejU++ugj3LhxAx4eHvD09ATwdIQrn62tLQDA2tpa4xTZnDlzsGDBAvTt2xfA0xG8CxcuYOXKlRrJ3Pjx46UyRfnqq68QGBiIMWPGAAAmTpyIqKgofPXVVxrJ3MCBAzFs2LBS971t27b4+OOPAQD16tXDP//8g0WLFmn88Bdsy9/fHx9//LEUd+3atTFnzhxMmTIFwcHBiIiIQGxsLOLj4+Hs7AwAmDt3rnQ8i/LNN99ApVJh06ZN0mncevXqSeuNjIyQmZlZ4mnHQYMGYeHChbh+/TpcXV2Rl5eHTZs2Yfr06QCAAwcO4OzZs0hKSoJSqZSO52+//YZffvlFSkiLMnnyZPj4+AAAPvzwQwwYMAD79u1D27ZtAQDDhw/HmjVrpPKlfd4bNmxAbm4ufvjhBxgbG6NRo0a4desWRo8eXWwMNWrUwOTJk6X348aNw+7du7FlyxaNZK5p06YIDg4GANStWxfLli3Dvn37mMwREVHZ2JoZ6sR2d+zYAVNTU+Tk5CA7Oxu9e/fG0qVLcfXqVWRnZ0s/0gAgl8vRsmVLxMbGAgBGjx6Nt956CydPnkT37t3Rp08ftGnTpthtJScn4+bNmxg+fDhGjhwpLc/JySl0cXt+glic2NjYQklH27ZtsWTJknK1k8/Ly6vQ+8WLF5fYVnR0NI4fP47PP/9cWpabm4snT57g8ePHiI2NRc2aNaVErqjtFBQTE4P27ds/1/V4Hh4eqF+/Pn766Sd8/PHHOHToEJKSktCvXz8p7kePHsHa2lqjXkZGBq5evVpi202bNpX+397eHgDQpEkTjWVJSUkAyvZ5x8bGolmzZjA2NpbWl3aMcnNz8cUXX2Dz5s24ffs2MjMzkZmZCRMTk2JjBQBHR0cptheFyRwR0X/I85zqfJk6deqEb7/9FnK5HE5OTlISkZCQAODptWLPEkJIy3x9fXH9+nXs3LkTERER6NKlCz744AN89dVXRW4rLy8PwNMRvmdHUAAUOgVa8Ie5KCXFVp52ytp+wbby8vIwe/bsIkcQDQ0NIYQotc2CnneSR75BgwZh48aN+Pjjj7Fx40b4+PjAxsZGitvR0REHDx4sVK+02788m2Tm70vBZfmfc1k+76KOUWkWLFiARYsWYfHixWjSpAlMTEwwfvx4ZGVlFRtrwdheFE6AICKil87ExAR16tSBq6urxo9fnTp1oFAo8Pfff0vLsrOzceLECTRo0EBaZmtri8DAQKxfvx6LFy/Gd999BwDSNXK5ublSWXt7e9SoUQPXrl1DnTp1NF75EybKqkGDBhqxAUBkZKRGbOURFRVV6H39+vVLrNO8eXNcunSp0L7UqVMHenp6aNiwIW7cuKFxTduRI0dKbLNp06Y4fPiwdN1iQQqFQuOYFmfgwIE4e/YsoqOj8csvv2DQoEEacScmJsLAwKBQ3PkJX0Uoy+fdsGFDnD59WrrWEij8WRR0+PBh9O7dG4MHD0azZs1Qu3ZtXL58ucLifh4cmSMioirDxMQEo0ePxkcffQQrKyvUrFkToaGhePz4MYYPHw4AmDVrFlq0aIFGjRohMzMTO3bskJIpOzs7GBkZYffu3XB2doahoSFUKhVCQkIQFBQEc3Nz+Pr6IjMzEydOnEBqaiomTpxY5vg++ugj9OvXD82bN0eXLl2wfft2bN26FREREVrt7z///IPQ0FD06dMH4eHh2LJlC3bu3FlinVmzZsHPzw8uLi545513oKenhzNnzuDs2bP47LPP0LVrV7i7u2PIkCFYsGAB0tLSMGPGjBLbHDt2LJYuXYp3330X06ZNg0qlQlRUFFq2bAl3d3fUqlULe/bswaVLl2BtbV3svdfc3NzQpk0bDB8+HDk5Oejdu7e0rmvXrvDy8kKfPn3w5Zdfwt3dHXfu3MGuXbvQp0+fMp+aLovSPu+BAwdixowZGD58OD755BPEx8cXO7Kbr06dOvj1118RGRkJS0tLLFy4EImJiVon8hWJI3NERFSlfPHFF3jrrbfg7++P5s2b48qVK9izZw8sLS0BPB0lmjZtGpo2bYoOHTpAX18fmzZtAgAYGBjg66+/xsqVK+Hk5CQlEyNGjMD333+PNWvWoEmTJvD29saaNWvKPTLXp08fLFmyBPPnz0ejRo2wcuVKhIWFadwCpTwmTZqE6OhoeHh4SBft51/oXxwfHx/s2LED4eHheO2119C6dWssXLgQrq6uAJ7OLN22bRsyMzPRsmVLjBgxQuP6uqJYW1tj//79ePToEby9vdGiRQusWrVKGjUdOXIk3N3d4enpCVtbW/zzzz/FtjVo0CCcPn0affv21Th9K5PJsGvXLnTo0AHDhg1DvXr18O677yI+Pl66Dq6ilPZ5m5qaYvv27bhw4QI8PDwwY8YMfPnllyW2OXPmTDRv3hw+Pj7o2LEjHBwcCt1IudKISjR37lzh6ekpTE1Nha2trejdu7e4ePGiRpm8vDwRHBwsHB0dhaGhofD29hbnzp3TKPPkyRMxduxYYW1tLYyNjUXPnj3FzZs3Ncrcv39fDB48WJibmwtzc3MxePBgkZqaWuZY1Wq1ACDUarXW+/tfIjezEgCE3MzqpdQjosIyMjLEhQsXREZGRmWHQlpwdXUVixYtquwwqBKV9DdcnryjUkfmDh06hA8++ABRUVEIDw9HTk4OunfvrvFIl9DQUCxcuBDLli3D8ePH4eDggG7duuHhw4dSmfHjx2Pbtm3YtGkT/v77bzx69Ah+fn4a5/cHDhyImJgY7N69G7t370ZMTAz8/f1f6v4SERERVbRKvWZu9+7dGu/DwsJgZ2eH6OhodOjQAUIILF68GDNmzJBm7axduxb29vbYuHEj3nvvPajVaqxevRrr1q1D165dAQDr16+Hi4sLIiIi4OPjg9jYWOzevRtRUVHSzJZVq1bBy8sLly5dgru7+8vdcSIiIqIKUqWumVOr1QAg3QU6Li4OiYmJ6N69u1RGqVTC29sbkZGRAJ7etyY7O1ujjJOTExo3biyVOXLkCFQqlcYU5datW0OlUkllCsrMzERaWprGi4iIqKLEx8eX+mQGorKoMsmcEAITJ05Eu3bt0LhxYwBAYmIiABS6MNLe3l5al5iYCIVCIV0YW1wZOzu7Qtu0s7OTyhQ0b948qFQq6eXi4vJ8O0hERET0AlSZZG7s2LE4c+ZMoYfWAmW7QWNBBcsUVb6kdqZNmwa1Wi29bt68WZbdICJ66YQWN0AlospXUX+7VSKZGzduHP744w8cOHBA4/Ej+c+AKzh6lpSUJI3WOTg4ICsrC6mpqSWWuXv3bqHtJicnFzsdWqlUwtzcXONFRFSV5N/NvuAd6IlIN+T/7RZ8Ekl5VeoECCEExo0bh23btuHgwYOF7vfj5uYGBwcHhIeHw8PDA8DTHT906JB0P5gWLVpALpcjPDxcev5bQkICzp07h9DQUABPn7emVqtx7NgxtGzZEgBw9OhRqNXqEp/nR0RUlRkYGMDY2BjJycmQy+XQ06sS/z4nojLIy8tDcnIyjI2NYWDwfOlYpSZzH3zwATZu3Ijff/8dZmZm0gicSqWCkZERZDIZxo8fj7lz56Ju3bqoW7cu5s6dC2NjYwwcOFAqO3z4cEyaNAnW1tawsrLC5MmT0aRJE2l2a4MGDdCjRw+MHDkSK1euBACMGjUKfn5+nMlKRDpLJpPB0dERcXFxuH79emWHQ0TlpKenh5o1a5Z66VhpKjWZ+/bbbwGg0J2zw8LCEBgYCACYMmUKMjIyMGbMGKSmpqJVq1bYu3cvzMzMpPKLFi2CgYEB+vXrh4yMDHTp0gVr1qzRGLbcsGEDgoKCpFmvvXr1wrJly17sDhIRvWAKhQJ169blqVYiHaRQKCpkRF0meOVsmaSlpUGlUkGtVvP6OQAKc2tkP7wPuZkVstJSXng9IiKi6qQ8eQcvsCAiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdFilJnN//fUXevbsCScnJ8hkMvz2228a6wMDAyGTyTRerVu31iiTmZmJcePGwcbGBiYmJujVqxdu3bqlUSY1NRX+/v5QqVRQqVTw9/fHgwcPXvDeEREREb14BpW58fT0dDRr1gxDhw7FW2+9VWSZHj16ICwsTHqvUCg01o8fPx7bt2/Hpk2bYG1tjUmTJsHPzw/R0dHQ19cHAAwcOBC3bt3C7t27AQCjRo2Cv78/tm/f/oL2jMoi6VFOuevI9WWwNNIvd73UjFxk54py13uebRIREb0MlZrM+fr6wtfXt8QySqUSDg4ORa5Tq9VYvXo11q1bh65duwIA1q9fDxcXF0RERMDHxwexsbHYvXs3oqKi0KpVKwDAqlWr4OXlhUuXLsHd3b1id4rKRAjgp7Nqreq+Uc8U5sqyJ1cPM3Ox499HWm0r35BXLZjQERFRlVSpyVxZHDx4EHZ2drCwsIC3tzc+//xz2NnZAQCio6ORnZ2N7t27S+WdnJzQuHFjREZGwsfHB0eOHIFKpZISOQBo3bo1VCoVIiMji03mMjMzkZmZKb1PS0t7QXtI5bXzORMzbWg7qkdERPSiVelkztfXF++88w5cXV0RFxeHmTNnonPnzoiOjoZSqURiYiIUCgUsLS016tnb2yMxMREAkJiYKCV/z7Kzs5PKFGXevHmYPXt2xe4QaXiSk1fuOnoyGRT6snLXy8oVyBPaJWR6svJvj4iI6GWp0slc//79pf9v3LgxPD094erqip07d6Jv377F1hNCQPbMD7CsiB/jgmUKmjZtGiZOnCi9T0tLg4uLS3l3gYohAFy5n1XuenoyoKZKAYNyTN3JzhW4m55b7m09K+VxDuxMq/SfCxERVVM69evk6OgIV1dXXL58GQDg4OCArKwspKamaozOJSUloU2bNlKZu3fvFmorOTkZ9vb2xW5LqVRCqVRW8B7Qs4Njt9TZWrWRlSNgUM7Rudw8AX097UbY9GRPR/aIiIiqIq2Sudq1a+P48eOwtrbWWP7gwQM0b94c165dq5DgCkpJScHNmzfh6OgIAGjRogXkcjnCw8PRr18/AEBCQgLOnTuH0NBQAICXlxfUajWOHTuGli1bAgCOHj0KtVotJXz08uWmp+LAeC+t6ipVtmgz+49y1cnKzYOWZ1khkwH30ss/85aIiOhl0CqZi4+PR25u4dNWmZmZuH37dpnbefToEa5cuSK9j4uLQ0xMDKysrGBlZYWQkBC89dZbcHR0RHx8PKZPnw4bGxu8+eabAACVSoXhw4dj0qRJsLa2hpWVFSZPnowmTZpIs1sbNGiAHj16YOTIkVi5ciWAp7cm8fPz40zWyiQEMlOLv2axJJmpieVKBIUQyHvOgbVrTg6IuxDzfI0QERG9AOVK5v7443+jIXv27IFKpZLe5+bmYt++fahVq1aZ2ztx4gQ6deokvc+/Ri0gIADffvstzp49ix9//BEPHjyAo6MjOnXqhM2bN8PMzEyqs2jRIhgYGKBfv37IyMhAly5dsGbNGukecwCwYcMGBAUFSbNee/XqhWXLlpVn16mCGJhYQEhDZOU8VZp+X/p/bRNBbd3T52lWIiKqmmRClP3kk57e06vOZTIZClaTy+WoVasWFixYAD8/v4qNsgpIS0uDSqWCWq2Gubl5ZYdT6RTm1sh+eB9yMytkpaWUud6rM7YiRZ2u1Tbv/jwLeY+1uzedtnIfPwCEKPd+EhERPY/y5B3lGpnLy3t6Kwk3NzccP34cNjY22kdJ1VK35u7Y/PcFiLzyzy61HzDvBURUsoTVY5Cbnqr19XZEREQvmlbXzMXFxVV0HFRN9GhWEyfTjLRKjp7eYuRlT0TgPeaIiKhq0/rWJPv27cO+ffuQlJQkjdjl++GHH547MPpvsjExQB1rpVYTErJzBRQGMq0SwZw8AQMtbk1yjbkcERFVcVolc7Nnz8ann34KT09PODo6lnjzXaJnKfRlUOiX446/zzA0gNaJoLb3mfvr///LLk5ERFWVVsncihUrsGbNGvj7+1d0PPQfZ21sgJoquVaP1np6mlX7bTuYGpTryREAT7ISEVHVp1Uyl5WVxRvuvgCpGblaP9Bdri+DpZF+6QWrgKfPVi1/mmRoACgN9LRKBLV9pisREVFVp1UyN2LECGzcuBEzZ86s6HiqrdSMXPwY8+C52hjyqkWVT+jkz5lQaZsIEhER/Vdplcw9efIE3333HSIiItC0aVPI5XKN9QsXLqyQ4KqT7FyBrFyh1agT8HTkSdtRvZfJ0kgfQ1610DrWjJw8GJX3XOlz1PuIeSMREVVxWiVzZ86cwauvvgoAOHfunMY6TobQTsrjHNzQ8sHzz7ZhZ6r1BOWXpqqPHhIREekSrX75Dxw4UNFxVHtZuQJZuXlaP0NUT/a0DSIiIqpeqv4wTjVxLz0HV1IytX7SgEz2tA0iIiKqXrRK5jp16lTi6dT9+/drHVB1dSstGzef8zTrrbTnq09ERES6R6tkLv96uXzZ2dmIiYnBuXPnEBAQUBFxVTvf/H4YGQ8ytK4v09PHqasNENDcqgKjIiIioqpOq2Ru0aJFRS4PCQnBo0ePniug6ursyonIfpT6XG18t8kKi9+5UkERERERkS6o0GvmBg8ejJYtW+Krr76qyGarhdzHauSmP18yl82ZxERERNVOhSZzR44cgaGhYUU2WW2YGyuR8hCATA9KC7ty1c18cBdaz5wgIiIinaZVMte3b1+N90IIJCQk4MSJE3wqhJbyx9SUFnbotPhIueqGf9ACuY/uV3xQREREVOVplcypVCqN93p6enB3d8enn36K7t27V0hg1Y2+3v/+66ySl1y4GDzLSkREVP1olcyFhYVVdBzVnt7/Z2IGejLUsVKUqy5zOCIiourrua6Zi46ORmxsLGQyGRo2bAgPD4+KiqvakgEw1OIZokRERFQ9aZXMJSUl4d1338XBgwdhYWEBIQTUajU6deqETZs2wdbWtqLj/M/T+//hNWO5HgY0UZVcuAA+DJ6IiKj60moIaNy4cUhLS8P58+dx//59pKam4ty5c0hLS0NQUFBFx1it6MkAO1ODcr2IiIio+tIqE9i9ezciIiLQoEEDaVnDhg3xzTffcAIEERER0UukVTKXl5cHubzwjEu5XI68vLznDopentSMXGTn8h51REREukqrZK5z58748MMP8dNPP8HJyQkAcPv2bUyYMAFdunSp0ADpxUnNyMWPMQ+0qst7FBMREVUNWiVzy5YtQ+/evVGrVi24uLhAJpPhxo0baNKkCdavX1/RMdILkp0rkJUrkMfMjIiISGdplcy5uLjg5MmTCA8Px8WLFyGEQMOGDdG1a9eKjo9eoJTHObihztaqLtM/IiKiqqFcydz+/fsxduxYREVFwdzcHN26dUO3bt0AAGq1Go0aNcKKFSvQvn37FxIsVaysXIGs3DzkMTMjIiLSWeVK5hYvXoyRI0fC3Ny80DqVSoX33nsPCxcuZDKnI+6l5+BKSuZzXf/GM7RERESVq1zJ3OnTp/Hll18Wu7579+746quvnjsoejlupWXjppanWXmelYiIqGooVzJ39+7dIm9JIjVmYIDk5OTnDopejpNX7uDJ9ViIvFwtajObIyIiqgrKlczVqFEDZ8+eRZ06dYpcf+bMGTg6OlZIYPTirfqwD7Ie3teqbu7jBwAAGR8lRkREVKnKlcy9/vrrmDVrFnx9fWFoaKixLiMjA8HBwfDz86vQAOnFyX6Uitz01OdqQ1nCSC0RERG9eOVK5j755BNs3boV9erVw9ixY+Hu7g6ZTIbY2Fh88803yM3NxYwZM15UrPSiyGRQWthrVdXJiSOxRERElalcyZy9vT0iIyMxevRoTJs2DeL/pzLKZDL4+Phg+fLlsLfXLimgly//FKm+iSX8w06Uu76eDBjzmlUFR0VERETloVfeCq6urti1axfu3buHo0ePIioqCvfu3cOuXbtQq1atcrX1119/oWfPnnBycoJMJsNvv/2msV4IgZCQEDg5OcHIyAgdO3bE+fPnNcpkZmZi3LhxsLGxgYmJCXr16oVbt25plElNTYW/vz9UKhVUKhX8/f3x4MGD8u76f5qhgV65Xwp9PSj0edEcERFRZSp3MpfP0tISr732Glq2bAlLS0ut2khPT0ezZs2wbNmyIteHhoZi4cKFWLZsGY4fPw4HBwd069YNDx8+lMqMHz8e27Ztw6ZNm/D333/j0aNH8PPzQ27u/2ZoDhw4EDExMdi9ezd2796NmJgY+Pv7axXzf5EMgLO5QblfNVVyWBtr9RARIiIiqiCV+kvs6+sLX1/fItcJIbB48WLMmDEDffv2BQCsXbsW9vb22LhxI9577z2o1WqsXr0a69atkx4ltn79eri4uCAiIgI+Pj6IjY3F7t27ERUVhVatWgEAVq1aBS8vL1y6dAnu7u4vZ2erOEMD7fJ6OUfmiIiIKlWVHVaJi4tDYmIiunfvLi1TKpXw9vZGZGQk3nvvPURHRyM7O1ujjJOTExo3bozIyEj4+PjgyJEjUKlUUiIHAK1bt4ZKpUJkZGSxyVxmZiYyMzOl92lpaS9gLyte0qOccteRyYABTVTlrifXl8HSSL/c9YiIiKjiVNlkLjExEQAKTaiwt7fH9evXpTIKhaLQaV57e3upfmJiIuzs7Aq1b2dnJ5Upyrx58zB79uzn2oeXTQjgp7PqcpXPZ2daZbsCERERlUDra+ZeFlmBu9IKIQotK6hgmaLKl9bOtGnToFarpdfNmzfLGTkRERHRi1dlh2McHBwAPB1Ze/apEklJSdJonYODA7KyspCamqoxOpeUlIQ2bdpIZe7evVuo/eTk5BJvo6JUKqFUKitkX16mJzl5lR0CERERvURVdmTOzc0NDg4OCA8Pl5ZlZWXh0KFDUqLWokULyOVyjTIJCQk4d+6cVMbLywtqtRrHjh2Tyhw9ehRqtVoq818hAFy5n1XmF5+uSkREpPsqdWTu0aNHuHLlivQ+Li4OMTExsLKyQs2aNTF+/HjMnTsXdevWRd26dTF37lwYGxtj4MCBAACVSoXhw4dj0qRJsLa2hpWVFSZPnowmTZpIs1sbNGiAHj16YOTIkVi5ciUAYNSoUfDz8/vPzGR99tq3qymZxRcsQ30iIiLSLZWazJ04cQKdOnWS3k+cOBEAEBAQgDVr1mDKlCnIyMjAmDFjkJqailatWmHv3r0wMzOT6ixatAgGBgbo168fMjIy0KVLF6xZswb6+v+bZblhwwYEBQVJs1579epV7L3tdJoAbqVll6s8ERER6TaZEByXKYu0tDSoVCqo1WqYm5tXePvOzs64ffs2atSoUegJFqWRm1kj59F96JtYwnH48jLXS1g9BrnpqTAwtUL2w5TyhlwtKMytkf3wPuRmVshK4zEiIqKXozx5R5WdAEFllz8pN/fxAySsHlPmermPH2jUJyIiIt3DZO4/QCmXIxsAhEBueqpW9YmIiEg3MZn7D3BycsJ1of3wmpOTY+mFiIiIqEpiMvcf8Nu+SCyOSkGeFlc/6smA8a2tKz4oIiIieimYzP0HWBsboI6VEnlazGXRk8lgbcxuQEREpKv4K/4fodCXAeBMBiIiouqmyj4BgspOrv98Sdzz1iciIqLKw5G5/wBLI30MedUC2bnlP80q15fB0ki/9IJERERUJTGZ+49gQkZERFQ98TQrERERkQ5jMkdERESkw5jMEREREekwJnNEREREOozJHBEREZEOYzJHREREpMOYzBERERHpMCZzRERERDqMyRwRERGRDmMyR0RERKTDmMwRERER6TAmc0REREQ6jMkcERERkQ5jMkdERESkw5jMEREREekwJnNEREREOozJHBEREZEOYzJHREREpMOYzBERERHpMCZzRERERDqMyRwRERGRDmMyR0RERKTDmMwRERER6TAmc0REREQ6jMkcERERkQ6r0slcSEgIZDKZxsvBwUFaL4RASEgInJycYGRkhI4dO+L8+fMabWRmZmLcuHGwsbGBiYkJevXqhVu3br3sXSEiIiJ6Iap0MgcAjRo1QkJCgvQ6e/astC40NBQLFy7EsmXLcPz4cTg4OKBbt254+PChVGb8+PHYtm0bNm3ahL///huPHj2Cn58fcnNzK2N3iIiIiCqUQWUHUBoDAwON0bh8QggsXrwYM2bMQN++fQEAa9euhb29PTZu3Ij33nsParUaq1evxrp169C1a1cAwPr16+Hi4oKIiAj4+Pi81H0hIiIiqmhVfmTu8uXLcHJygpubG959911cu3YNABAXF4fExER0795dKqtUKuHt7Y3IyEgAQHR0NLKzszXKODk5oXHjxlKZ4mRmZiItLU3jRURERFTVVOlkrlWrVvjxxx+xZ88erFq1ComJiWjTpg1SUlKQmJgIALC3t9eoY29vL61LTEyEQqGApaVlsWWKM2/ePKhUKunl4uJSgXtGREREVDGqdDLn6+uLt956C02aNEHXrl2xc+dOAE9Pp+aTyWQadYQQhZYVVJYy06ZNg1qtll43b97Uci+IiIiIXpwqncwVZGJigiZNmuDy5cvSdXQFR9iSkpKk0ToHBwdkZWUhNTW12DLFUSqVMDc313gRERERVTU6lcxlZmYiNjYWjo6OcHNzg4ODA8LDw6X1WVlZOHToENq0aQMAaNGiBeRyuUaZhIQEnDt3TipDREREpMuq9GzWyZMno2fPnqhZsyaSkpLw2WefIS0tDQEBAZDJZBg/fjzmzp2LunXrom7dupg7dy6MjY0xcOBAAIBKpcLw4cMxadIkWFtbw8rKCpMnT5ZO2xIRERHpuiqdzN26dQsDBgzAvXv3YGtri9atWyMqKgqurq4AgClTpiAjIwNjxoxBamoqWrVqhb1798LMzExqY9GiRTAwMEC/fv2QkZGBLl26YM2aNdDX16+s3SIiIiKqMDIhhKjsIHRBWloaVCoV1Gr1C7l+ztnZGbdv30aNGjX4hIoqRGFujeyH9yE3s0JWWkplh0NERNVEefIOnbpmjoiIiIg0MZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh0GJM5IiIiIh3GZI6IiIhIhzGZIyIiItJhTOaIiIiIdBiTOSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHRYtUrmli9fDjc3NxgaGqJFixY4fPhwZYdERERE9FyqTTK3efNmjB8/HjNmzMCpU6fQvn17+Pr64saNG5UdGhEREZHWqk0yt3DhQgwfPhwjRoxAgwYNsHjxYri4uODbb7+t7NCIiIiItGZQ2QG8DFlZWYiOjsbHH3+ssbx79+6IjIwssk5mZiYyMzOl92q1GgCQlpb2QmLMy8uT/vuitkHlJ4QAAGQ/vA8DU6tKjoaIiF4EmQwwUSrKXa//sPcw/5NJLyCi/+Ub+b9DJRLVwO3btwUA8c8//2gs//zzz0W9evWKrBMcHCwA8MUXX3zxxRdffFXa6+bNm6XmOdViZC6fTCbTeC+EKLQs37Rp0zBx4kTpfV5eHu7fvw9ra+ti6zyPtLQ0uLi44ObNmzA3N6/w9v8LeIxKxuNTOh6j0vEYlYzHp3Q8RiUr6/ERQuDhw4dwcnIqtc1qkczZ2NhAX18fiYmJGsuTkpJgb29fZB2lUgmlUqmxzMLC4kWFKDE3N2fnLwWPUcl4fErHY1Q6HqOS8fiUjseoZGU5PiqVqkxtVYsJEAqFAi1atEB4eLjG8vDwcLRp06aSoiIiIiJ6ftViZA4AJk6cCH9/f3h6esLLywvfffcdbty4gffff7+yQyMiIiLSWrVJ5vr374+UlBR8+umnSEhIQOPGjbFr1y64urpWdmgAnp7WDQ4OLnRql/6Hx6hkPD6l4zEqHY9RyXh8SsdjVLIXcXxkQpRlzisRERERVUXV4po5IiIiov8qJnNEREREOozJHBEREZEOYzJHREREpMOYzFURy5cvh5ubGwwNDdGiRQscPny4skOqMkJCQiCTyTReDg4OlR1Wpfnrr7/Qs2dPODk5QSaT4bffftNYL4RASEgInJycYGRkhI4dO+L8+fOVE2wlKe0YBQYGFupTrVu3rpxgK8G8efPw2muvwczMDHZ2dujTpw8uXbqkUaY696OyHJ/q3oe+/fZbNG3aVLrxrZeXF/78809pfXXuP0Dpx6ei+w+TuSpg8+bNGD9+PGbMmIFTp06hffv28PX1xY0bNyo7tCqjUaNGSEhIkF5nz56t7JAqTXp6Opo1a4Zly5YVuT40NBQLFy7EsmXLcPz4cTg4OKBbt254+PDhS4608pR2jACgR48eGn1q165dLzHCynXo0CF88MEHiIqKQnh4OHJyctC9e3ekp6dLZapzPyrL8QGqdx9ydnbGF198gRMnTuDEiRPo3LkzevfuLSVs1bn/AKUfH6CC+8/zPcKeKkLLli3F+++/r7Gsfv364uOPP66kiKqW4OBg0axZs8oOo0oCILZt2ya9z8vLEw4ODuKLL76Qlj158kSoVCqxYsWKSoiw8hU8RkIIERAQIHr37l0p8VRFSUlJAoA4dOiQEIL9qKCCx0cI9qGiWFpaiu+//579pxj5x0eIiu8/HJmrZFlZWYiOjkb37t01lnfv3h2RkZGVFFXVc/nyZTg5OcHNzQ3vvvsurl27VtkhVUlxcXFITEzU6E9KpRLe3t7sTwUcPHgQdnZ2qFevHkaOHImkpKTKDqnSqNVqAICVlRUA9qOCCh6ffOxDT+Xm5mLTpk1IT0+Hl5cX+08BBY9PvorsP9XmCRBV1b1795Cbmwt7e3uN5fb29khMTKykqKqWVq1a4ccff0S9evVw9+5dfPbZZ2jTpg3Onz8Pa2vryg6vSsnvM0X1p+vXr1dGSFWSr68v3nnnHbi6uiIuLg4zZ85E586dER0dXe3uWi+EwMSJE9GuXTs0btwYAPvRs4o6PgD7EACcPXsWXl5eePLkCUxNTbFt2zY0bNhQStiqe/8p7vgAFd9/mMxVETKZTOO9EKLQsurK19dX+v8mTZrAy8sLr7zyCtauXYuJEydWYmRVF/tTyfr37y/9f+PGjeHp6QlXV1fs3LkTffv2rcTIXr6xY8fizJkz+PvvvwutYz8q/viwDwHu7u6IiYnBgwcP8OuvvyIgIACHDh2S1lf3/lPc8WnYsGGF9x+eZq1kNjY20NfXLzQKl5SUVOhfNfSUiYkJmjRpgsuXL1d2KFVO/ixf9qfycXR0hKura7XrU+PGjcMff/yBAwcOwNnZWVrOfvRUccenKNWxDykUCtSpUweenp6YN28emjVrhiVLlrD//L/ijk9Rnrf/MJmrZAqFAi1atEB4eLjG8vDwcLRp06aSoqraMjMzERsbC0dHx8oOpcpxc3ODg4ODRn/KysrCoUOH2J9KkJKSgps3b1abPiWEwNixY7F161bs378fbm5uGuurez8q7fgUpbr1oaIIIZCZmVnt+09x8o9PUZ67/1TYVArS2qZNm4RcLherV68WFy5cEOPHjxcmJiYiPj6+skOrEiZNmiQOHjworl27JqKiooSfn58wMzOrtsfn4cOH4tSpU+LUqVMCgFi4cKE4deqUuH79uhBCiC+++EKoVCqxdetWcfbsWTFgwADh6Ogo0tLSKjnyl6ekY/Tw4UMxadIkERkZKeLi4sSBAweEl5eXqFGjRrU5RqNHjxYqlUocPHhQJCQkSK/Hjx9LZapzPyrt+LAPCTFt2jTx119/ibi4OHHmzBkxffp0oaenJ/bu3SuEqN79R4iSj8+L6D9M5qqIb775Rri6ugqFQiGaN2+uMQW+uuvfv79wdHQUcrlcODk5ib59+4rz589XdliV5sCBAwJAoVdAQIAQ4ultJYKDg4WDg4NQKpWiQ4cO4uzZs5Ub9EtW0jF6/Pix6N69u7C1tRVyuVzUrFlTBAQEiBs3blR22C9NUccGgAgLC5PKVOd+VNrxYR8SYtiwYdJvlq2trejSpYuUyAlRvfuPECUfnxfRf2RCCKHdmB4RERERVTZeM0dERESkw5jMEREREekwJnNEREREOozJHBEREZEOYzJHREREpMOYzBERERHpMCZzRERERDqMyRwRERGRDmMyR0RERKTDmMwR0QsVGBgImUwGmUwGAwMD1KxZE6NHj0Zqamplh6bzAgMD0adPn8oOg4gqGZM5InrhevTogYSEBMTHx+P777/H9u3bMWbMmMoOiyqQEAI5OTmVHQZRtcRkjoheOKVSCQcHBzg7O6N79+7o378/9u7dq1EmLCwMDRo0gKGhIerXr4/ly5dL67KysjB27Fg4OjrC0NAQtWrVwrx586T1MpkM3377LXx9fWFkZAQ3Nzds2bJFo/2zZ8+ic+fOMDIygrW1NUaNGoVHjx5J6/NHub766is4OjrC2toaH3zwAbKzs6Uyy5cvR926dWFoaAh7e3u8/fbb0johBEJDQ1G7dm0YGRmhWbNm+OWXX0o8LpmZmZgyZQpcXFygVCpRt25drF69GgCQm5uL4cOHw83NDUZGRnB3d8eSJUukuiEhIVi7di1+//13aeTz4MGDAIDbt2+jf//+sLS0hLW1NXr37o34+Hipbk5ODoKCgmBhYQFra2tMnToVAQEBGqN8mZmZCAoKgp2dHQwNDdGuXTscP35cWn/w4EHIZDLs2bMHnp6eUCqVWLduHfT09HDixAmN/Vy6dClcXV3BR4ETvSCCiOgFCggIEL1795beX716VTRs2FDY29tLy7777jvh6Ogofv31V3Ht2jXx66+/CisrK7FmzRohhBDz588XLi4u4q+//hLx8fHi8OHDYuPGjVJ9AMLa2lqsWrVKXLp0SXzyySdCX19fXLhwQQghRHp6unBychJ9+/YVZ8+eFfv27RNubm4iICBAI05zc3Px/vvvi9jYWLF9+3ZhbGwsvvvuOyGEEMePHxf6+vpi48aNIj4+Xpw8eVIsWbJEqj99+nRRv359sXv3bnH16lURFhYmlEqlOHjwYLHHpl+/fsLFxUVs3bpVXL16VURERIhNmzYJIYTIysoSs2bNEseOHRPXrl0T69evF8bGxmLz5s1CCCEePnwo+vXrJ3r06CESEhJEQkKCyMzMFOnp6aJu3bpi2LBh4syZM+LChQti4MCBwt3dXWRmZgohhPjss8+ElZWV2Lp1q4iNjRXvv/++MDc31/icgoKChJOTk9i1a5c4f/68CAgIEJaWliIlJUUIIcSBAwcEANG0aVOxd+9eceXKFXHv3j3RrVs3MWbMGI399PDwELNmzSq5oxCR1pjMEdELFRAQIPT19YWJiYkwNDQUAAQAsXDhQqmMi4uLRnImhBBz5swRXl5eQgghxo0bJzp37izy8vKK3AYA8f7772ssa9WqlRg9erQQ4mmyaGlpKR49eiSt37lzp9DT0xOJiYlSnK6uriInJ0cq884774j+/fsLIYT49ddfhbm5uUhLSyu0/UePHglDQ0MRGRmpsXz48OFiwIABRcZ86dIlAUCEh4cXub4oY8aMEW+99Zb0vmCiLIQQq1evFu7u7hrHKjMzUxgZGYk9e/YIIYSwt7cX8+fPl9bn5OSImjVrSm09evRIyOVysWHDBqlMVlaWcHJyEqGhoUKI/yVzv/32m8b2N2/eLCwtLcWTJ0+EEELExMQImUwm4uLiyryfRFQ+PM1KRC9cp06dEBMTg6NHj2LcuHHw8fHBuHHjAADJycm4efMmhg8fDlNTU+n12Wef4erVqwCengKNiYmBu7s7goKCCp2iBQAvL69C72NjYwEAsbGxaNasGUxMTKT1bdu2RV5eHi5duiQta9SoEfT19aX3jo6OSEpKAgB069YNrq6uqF27Nvz9/bFhwwY8fvwYAHDhwgU8efIE3bp109iHH3/8UdqHgmJiYqCvrw9vb+9ij9uKFSvg6ekJW1tbmJqaYtWqVbhx40bxBxpAdHQ0rly5AjMzMykOKysrPHnyBFevXoVarcbdu3fRsmVLqY6+vj5atGghvb969Sqys7PRtm1baZlcLkfLli2lY5rP09NT432fPn1gYGCAbdu2AQB++OEHdOrUCbVq1SoxbiLSnkFlB0BE/30mJiaoU6cOAODrr79Gp06dMHv2bMyZMwd5eXkAgFWrVqFVq1Ya9fITq+bNmyMuLg5//vknIiIi0K9fP3Tt2rXUa9JkMhmAp9ez5f9/cWWApwlLwXX58ZmZmeHkyZM4ePAg9u7di1mzZiEkJATHjx+XyuzcuRM1atTQaEOpVBa5XSMjoxJj//nnnzFhwgQsWLAAXl5eMDMzw/z583H06NES6+Xl5aFFixbYsGFDoXW2trYa+/Ys8cz1bPn/X1SZgsueTZABQKFQwN/fH2FhYejbty82btyIxYsXlxgzET0fjswR0UsXHByMr776Cnfu3IG9vT1q1KiBa9euoU6dOhovNzc3qY65uTn69++PVatWYfPmzfj1119x//59aX1UVJTGNqKiolC/fn0AQMOGDRETE4P09HRp/T///AM9PT3Uq1evzHEbGBiga9euCA0NxZkzZxAfH4/9+/ejYcOGUCqVuHHjRqF9cHFxKbKtJk2aIC8vD4cOHSpy/eHDh9GmTRuMGTMGHh4eqFOnTqFRPoVCgdzcXI1lzZs3x+XLl2FnZ1coFpVKBZVKBXt7exw7dkyqk5ubi1OnTknv69SpA4VCgb///ltalp2djRMnTqBBgwalHqcRI0YgIiICy5cvR3Z2Nvr27VtqHSLSHkfmiOil69ixIxo1aoS5c+di2bJlCAkJQVBQEMzNzeHr64vMzEycOHECqampmDhxIhYtWgRHR0e8+uqr0NPTw5YtW+Dg4AALCwupzS1btsDT0xPt2rXDhg0bcOzYMWlm6KBBgxAcHIyAgACEhIQgOTkZ48aNg7+/P+zt7csU844dO3Dt2jV06NABlpaW2LVrF/Ly8uDu7g4zMzNMnjwZEyZMQF5eHtq1a4e0tDRERkbC1NQUAQEBhdqrVasWAgICMGzYMHz99ddo1qwZrl+/jqSkJPTr1w916tTBjz/+iD179sDNzQ3r1q3D8ePHNRLcWrVqYc+ePbh06RKsra2hUqkwaNAgzJ8/H71798ann34KZ2dn3LhxA1u3bsVHH30EZ2dnjBs3DvPmzUOdOnVQv359LF26FKmpqdKom4mJCUaPHo2PPvoIVlZWqFmzJkJDQ/H48WMMHz681GPVoEEDtG7dGlOnTsWwYcNKHYUkoudUuZfsEdF/XVEX6QshxIYNG4RCoRA3btyQ3r/66qtCoVAIS0tL0aFDB7F161YhxNMJDK+++qowMTER5ubmokuXLuLkyZNSWwDEN998I7p16yaUSqVwdXUVP/30k8b2zpw5Izp16iQMDQ2FlZWVGDlypHj48GGJcX744YfC29tbCCHE4cOHhbe3t7C0tBRGRkaiadOm0sxSIYTIy8sTS5YsEe7u7kIulwtbW1vh4+MjDh06VOyxycjIEBMmTBCOjo5CoVCIOnXqiB9++EEIIcSTJ09EYGCgUKlUwsLCQowePVp8/PHHolmzZlL9pKQk0a1bN2FqaioAiAMHDgghhEhISBBDhgwRNjY2QqlUitq1a4uRI0cKtVothBAiOztbjB07VpibmwtLS0sxdepU8c4774h3331XI7Zx48ZJbbRt21YcO3ZMWp8/ASI1NbXIfVu9erUAoFGHiF4MmRC88Q8R6TaZTIZt27bxaQhaysvLQ4MGDdCvXz/MmTOnQtr8/PPPsWnTJpw9e7ZC2iOi4vE0KxFRNXP9+nXs3bsX3t7eyMzMxLJlyxAXF4eBAwc+d9uPHj1CbGwsli5dWmGJIRGVjBMgiIiqGT09PaxZswavvfYa2rZti7NnzyIiIqJMkxtKM3bsWLRr1w7e3t4YNmxYBURLRKXhaVYiIiIiHcaROSIiIiIdxmSOiIiISIcxmSMiIiLSYUzmiIiIiHQYkzkiIiIiHcZkjoiIiEiHMZkjIiIi0mFM5oiIiIh02P8B9KGfUaI6iusAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "idata_pps = model.predict(idata=idata, kind=\"pps\", inplace=False)\n", + "\n", + "bins = np.arange(35)\n", + "fig, ax = plt.subplots(figsize=(7, 3))\n", + "ax = plot_ppc_discrete(idata_pps, bins, ax)\n", + "ax.set_xlabel(\"Response category\")\n", + "ax.set_ylabel(\"Count\")\n", + "ax.set_title(\"Sequential model - Posterior Predictive Distribution\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrated how to fit cumulative and sequential ordinal regression models using Bambi. Cumulative models focus on modeling the cumulative probabilities of an ordinal outcome variable taking on values up to and including a certain category, whereas a sequential model focuses on modeling the probability that an ordinal outcome variable stops at a particular category, rather than continuing to higher categories. To achieve this, both models assume that the reponse variable originates from a categorization of a latent continuous variable $Z$. However, the cumulative model assumes that there are $K$ thresholds $\\tau_k$ that partition $Z$ into $K+1$ observable, ordered categories of $Y$. The sequential model assumes that for every category $k$ there is a latent continuous variable $Z$ that determines the transition between categories $k$ and $k+1$; thus, a threshold $\\tau$ belongs to each latent process.\n", + "\n", + "Cumulative models can be used in situations where the outcome variable is on the Likert scale, and you are interested in understanding the impact of predictors on the probability of reaching or exceeding specific categories. Sequential models are particularly useful when you are interested in understanding the predictors that influence the decision to stop at a specific response level. It's well-suited for analyzing data where categories represent stages, and the focus is on the transitions between these stages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Fri Sep 15 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.11.0\n", + "IPython version : 8.13.2\n", + "\n", + "bambi : 0.13.0.dev0\n", + "arviz : 0.15.1\n", + "numpy : 1.24.2\n", + "pandas : 2.0.1\n", + "matplotlib: 3.7.1\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bambinos", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/thumbnails/ordinal_regression.png b/docs/notebooks/thumbnails/ordinal_regression.png new file mode 100644 index 000000000..3e5b8e0fc Binary files /dev/null and b/docs/notebooks/thumbnails/ordinal_regression.png differ