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Parameter values of staff lifetime prediction models_
| Methods | Parameters | Values |
|---|---|---|
| SVM | C | 105 |
| SVM | Kernel | RBF |
| SVM | Gamma | Scaled |
| LightGBM | N_estimators | 100 |
| LightGBM | Num_leaves | 50 |
| LightGBM | Max_depth | None |
| LightGBM | Learning_rate | 0.1 |
| KNN | N_neighbours | 5 |
Attributes in the first dataset_
| Attribute | Description |
|---|---|
| ID | Staff ID |
| Gender | Gender of the staff |
| Marital status | Marital status of the staff |
| Birth Date | Birth date of the staff |
| Education level | Education level of the staff |
| Graduated School | School the staff graduated from |
| Graduated Department | Department the staff graduated from |
| Department | Department of staff in the company |
| Duty | Duty of the staff in the company |
| Total Working Years | Total years of experience of the staff |
| Work Experience 1 - Working Time | Years of work experience in the first company |
| Work Experience 2 - Working Time | Years of work experience in the second company |
| Work Experience 3 - Working Time | Years of work experience in the third company |
| Universal Entry Date | Entry date of the staff to Universal |
| Current Salary | Current salary of the staff |
| Weekly Working Hours | Hours per week the staff works |
| Job Satisfaction | Staff satisfaction score with his job |
| Internal Relationship Satisfaction | Internal relationship satisfaction score of the staff |
| Business Travel Frequency | The number of business trips of the staff |
| Address - District | District where the staff lives |
Parameter values of staff churn prediction models_
| Algorithms | Parameters | Values |
|---|---|---|
| LR | Penalty | 12 |
| LR | C | 105 |
| CatBoost | N_estimators | 100 |
| CatBoost | Max_depth | 6 |
| CatBoost | Iterations | 1000 |
| CatBoost | Learning_rate | 0.1 |
| ELM | Alpha | 10−3 |
| ELM | N neurons | 64.32 |
| ELM | Activation function | tanh |
Attributes in the second dataset_
| Attribute | Description |
|---|---|
| Staff Type | Type of the staff |
| Staff Subtype | Subtype of the staff |
| Gender | Gender of the staff |
| Marital Status | Marital status of the staff |
| Agreement | Agreement between company and staff |
| Education level | Education level of the staff |
| Department 1 | The department to which the staff is affiliated |
| Department 2 | The department where the staff works |
| Insured Unit | Insurance policy of the staff |
| Duty | The duty assigned to the staff |
| Address - Province | Province where the staff lives |
| Address - District | District where the staff lives |
| Age | Age of the staff |
Parameter values of staff lifetime prediction model developed using SVM, LightGBM and KNN_
| Methods | Parameters | Values |
|---|---|---|
| SVM | C | 1010 |
| SVM | Kernel | RBF |
| SVM | Gamma | Scaled |
| LightGBM | N_estimators | 50 |
| LightGBM | Num_leaves | 30 |
| LightGBM | Max_depth | None |
| LightGBM | Learning_rate | 0.1 |
| KNN | N_neighbours | 5 |
Parameter values staff churn prediction model developed using LR, CatBoost and ELM_
| Algorithm | Parameters | Values |
|---|---|---|
| LR | Penalty | 12 |
| LR | C | 105 |
| CatBoost | N_estimators | 100 |
| CatBoost | Max_depth | 6 |
| CatBoost | Iterations | 500 |
| CatBoost | Learning_rate | 0.03 |
| ELM | Alpha | 10−5 |
| ELM | N neurons | 64 |
| ELM | Activation Function | tanh |
Accuracy and F1-score values of staff churn prediction models developed using LR, Catboost and ELM_
| Approaches | Algorithms | MAE | MAPE (%) |
|---|---|---|---|
| First Approach | SVM | 1158.25 | 19.94 |
| First Approach | LightGBM | 1011.81 | 18.37 |
| First Approach | KNN | 999.96 | 18.35 |
| Second Approach | SVM | 1124.01 | 19.68 |
| Second Approach | LightGBM | 1038.58 | 19.03 |
| Second Approach | KNN | 1044.15 | 18.89 |
| Third Approach | SVM | 1087.65 | 19.07 |
| Third Approach | LightGBM | 1045.42 | 18.85 |
| Third Approach | KNN | 1068.99 | 19.19 |