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| Component | Weight | Description |
|---|---|---|
| Promotion Velocity | 25% | How frequently the employee gets promoted |
| Job Level Achievement | 20% | Current job level (1-5 scale) |
| Income-to-Experience Ratio | 20% | Earning power relative to years of experience |
| Satisfaction Composite | 15% | Combined job + environment satisfaction |
| Job Involvement | 10% | Level of engagement and involvement |
| Retention Signal | 10% | Whether the employee is retained (vs. attrited) |
| Metric | Value |
|---|---|
| Test R² | 0.8997 |
| Test RMSE | 4.0360 |
| Test MAE | 2.9716 |
| CV R² (mean ± std) | 0.8631 ± 0.0194 |
| Feature | Importance |
|---|---|
| YearsSinceLastPromotion | 0.3244 |
| MonthlyIncome | 0.2196 |
| YearsAtCompany | 0.0937 |
| JobLevel | 0.0893 |
| JobSatisfaction | 0.0704 |
| EnvironmentSatisfaction | 0.0633 |
| JobInvolvement | 0.0574 |
| TotalWorkingYears | 0.0316 |
| DailyRate | 0.0122 |
| Age | 0.0098 |
| YearsInCurrentRole | 0.0076 |
| DistanceFromHome | 0.0066 |
| MaritalStatus_Num | 0.0048 |
| JobRole_Num | 0.0042 |
| YearsWithCurrManager | 0.0022 |
| BusinessTravel_Num | 0.0021 |
| Department_Num | 0.0008 |
1import joblib
2import numpy as np
3from huggingface_hub import hf_hub_download
4
5# Download and load model
6model_path = hf_hub_download(
7 repo_id="dev91205/employee-growth-score-predictor",
8 filename="employee_growth_model.joblib"
9)
10model = joblib.load(model_path)
11
12# Feature order: Age, BusinessTravel_Num, DailyRate, Department_Num,
13# DistanceFromHome, EnvironmentSatisfaction, JobInvolvement, JobLevel,
14# JobRole_Num, JobSatisfaction, MaritalStatus_Num, MonthlyIncome,
15# TotalWorkingYears, YearsAtCompany, YearsInCurrentRole,
16# YearsSinceLastPromotion, YearsWithCurrManager
17
18employee = np.array([[30, 2, 800, 2, 5, 3, 3, 2, 5, 3, 1, 5000, 8, 5, 3, 1, 3]])
19score = model.predict(employee)
20print(f"Growth Score: {score[0]:.1f}/100")| Score Range | Category | Description |
|---|---|---|
| 75-100 | 🟢 HIGH GROWTH | Strong career trajectory, ready for advancement |
| 50-74 | 🟡 MODERATE GROWTH | Solid performance, potential for development |
| 25-49 | 🟠 LOW GROWTH | Needs attention, potential stagnation |
| 0-24 | 🔴 AT RISK | Requires immediate development support |
| Parameter | Value |
|---|---|
| learning_rate | 0.05 |
| max_depth | 3 |
| min_samples_split | 10 |
| n_estimators | 500 |
| subsample | 0.8 |