Employee Churn Prediction

Predict whether an employee will churn from the company or not, and analyze the factors responsible for, or having the most influence on, employee churn.

Dataset

The dataset is available on Kaggle.

Variable	Description
satisfaction_level	Employee-reported job satisfaction level [0–1]
last_evaluation	Score of employee's last performance review [0–1]
number_project	Number of projects the employee contributes to
average_monthly_hours	Average number of hours the employee worked per month
time_spend_company	How long the employee has been with the company (years)
work_accident	Whether or not the employee experienced an accident while at work
left	Whether or not the employee left the company
promotion_last_5years	Whether or not the employee was promoted in the last 5 years
department	The employee's department
salary	The employee's salary (U.S. dollars)

Results

Model	Precision	Recall	F1-Score	Accuracy
Logistic Regression	0.75	0.81	0.77	0.81
Naive Bayes	0.87	0.80	0.82	0.80
KNN	0.79	0.88	0.83	0.94
SVC	0.80	0.88	0.84	0.94
Decision Tree	0.78	0.92	0.85	0.94
Quadratic Discriminant Analysis	0.90	0.85	0.86	0.85
Random Forest	0.87	0.90	0.88	0.96
Gradient Boosting	0.92	0.87	0.89	0.97
XGBoost	0.91	0.89	0.90	0.97

Conclusion

The XGBoost model achieves the best F1-Score and Accuracy. Features such as last_evaluation, number_project, tenure, and overworked are most important in deciding employee churn.

Insights:

• Employees involved in more than 4 projects have a higher chance of leaving the company.
• Most employees leave between the 2nd and 5th years of employment.
• A significant proportion of employees who leave are either overworked with good evaluation scores or underworked with low evaluation scores.