The weights (coefficients) of the predictors according to the logistic regression model are: 1. years_at_company: 0.355685 2. average_monthly_hours: 0.186333 3. last_evaluation: 0.133492 4. promotion_last_5_years: -0.210134 5. number_of_projects: -0.312886 6. work_accident: -0.461024 7. satisfaction_level: -0.947610 Positive coefficients indicate that an increase in the predictor increases the likelihood of leaving, while negative coefficients indicate a decrease in the likelihood of leaving.

The Random Forest model achieved an accuracy score of 98.22%, with strong precision, recall, and F1-scores for both classes. The confusion matrix confirms its effectiveness in predicting employee turnover.

What are the most important predictors according to the random forest classifier?

The feature importance according to the XGBoost model is as follows: 1. years_at_company: 0.314989 2. satisfaction_level: 0.242575 3. number_of_projects: 0.214908 4. last_evaluation: 0.084811 5. average_monthly_hours: 0.077681 6. work_accident: 0.050537 7. promotion_last_5_years: 0.014501

The XGBoost model has the highest recall for predicting employees who left (92.04%), making it the best choice for identifying potential leavers. Random Forest follows closely with 91.04% recall, while Logistic Regression lags significantly at 20.56%. Actionable steps: 1. Focus on improving satisfaction levels, as it is a key predictor of employee retention. 2. Monitor employees with high workloads (projects and hours) to prevent burnout. 3. Address salary concerns, especially for low and medium earners. 4. Implement retention strategies for employees with longer tenures, as they are more likely to leave.