Analyzing how AI tool adoption impacts employee productivity and burnout risk using Python, EDA, and Machine Learning.
- Source: Kaggle
- 4,500 employees · 6 job roles · 15 features · 0 null values
| Finding | |
|---|---|
| 1 | High AI users do 54% less manual work than low AI users |
| 2 | Developers & Writers use AI most · Managers & Designers the least |
| 3 | Very high AI usage correlates with lower work-life balance scores |
| 4 | Managers have the highest burnout risk across all roles |
| 5 | High deadline pressure raises error rates by ~47% |
| Model | Target | Result |
|---|---|---|
| Random Forest Regressor | productivity_score |
R² = 0.87 |
| Random Forest Classifier | burnout_risk_level |
Accuracy = 82% |
Python · Pandas · Scikit-learn · Matplotlib · Seaborn · Jupyter
Author: Winnie Nduva