Employee Attrition Prediction

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Employee attrition refers to the loss of employees as a result of a natural process, such as retirement, resignation, position elimination, personal health, or other similar reasons. An employer will not fill the vacancy left by a former employee due to attrition.

Attrition Rate is a common metric for determining the magnitude of employee attrition. Attrition rate, also known as ‘churn rate,’ is the rate at which employees leave. It is the number of people who have left the company divided by the average number of employees over a period of time. It is usually expressed as a percentage (%).

Business Understanding

• Problem : In 2020, Company ‘X’ has a 36.4 percent employee attrition rate, while the usual attrition rate is 10.9 percent. This is not good because a high attrition rate will increase the workload of the remaining workers and cause many projects to also be delayed.
• Goals : Reducing employee attrition at Company ‘X’.
• Objective : Understanding the factors that most influence attrition rates in this company and make recommendations on what companies can do to reduce attrition rates.
• Business Metrics : Attrition rate

Data Understanding

The dataset contains information from 1470 employees. The information consists of employee attrition, employee profile, employee performance, and employee status. Details of the data features are age, daily rate, distance from home, education, employee count, employee number, environment satisfaction, hourly rate, job involvement, job level, job satisfaction, monthly income, monthly rate, number companies worked, percent salary hike, performance rating, relationship satisfaction, standard hours, stock option level, total working years, training times last year, work life balance, years at company, years in current role, years since last promotion, years with current manager, attrition, business travel, department, education field, gender, job role, marital status, over 18, over time.

Data source : Kaggle

Data Insights

Graph 1. Correlation Matrix between feature and target label

• Graph 1 is the correlation between the features in the dataset. The stronger the color, the larger the correlation magnitude. Based on the graph, Over Time has the highest positive correlation with attrition, while Total Working Years has the lowest negative correlation, and Business Travel has almost no correlation with attrition.

Graph 2. Percentage of Attrition Based on Over Time

• Over time employees have a higher rate of attrition than non-over time employees, according to the Over Time feature (Graph 2).

Graph 3. Percentage of Attrition Based On Job Role

• The Laboratory Technician role has the highest attrition percentage in the Job Role feature, while the Research Director role has the lowest (Graph 3).

Graph 4. Percentage of Attrition per Job Level

• Job level 1 has the highest attrition percentage in Job Level features, while job levels 4 and 5 have the lowest attrition percentage (Graph 4).

Graph 5. Percentage of Attrition per Marital Status

• The percentage of attrition in married employees is higher than in married and divorced employees, as shown in the Marital Status feature. Employees who are divorced have the lowest percentage of attrition (Graph 5).

Graph 6. Percentage of Attrition per Department

• In the Department feature, the highest to lowest attrition percentages are Research & Development, Sales, and Human Resources (Graph 6).

Graph 7. Percentage of Attrition per Gender

• According to the Gender feature, male employees have a higher rate of attrition than female employees (Graph 7).

Graph 8. Percentage of Attrition per Monthly Income

• In the Monthly Income feature, the higher the monthly income, the lower the employee attrition percentage (Graph 8).

Graph 9. Percentage of Attrition Based on Years In Current Role

• In the Years In Current Role feature, the longer the employee is in the current role, the lower the employee attrition percentage (Graph 9).

Graph 10. Percentage of Attrition Based on Years Since Last Promotion

• In the Years Since Last Promotion feature, the highest percentage of employee attrition is 0 years (Graph 10).

Graph 11. Percentage of Attrition Based on Number Companies Worked

• In the Number Companies Worked feature, the highest percentage of employee attrition is for employees with a number of companies worked of 2 and the lowest percentage is for employees with a number of companies worked of 8 (Graph 11).

Graph 12. Percentage of Attrition Based on Education Field

• Employees with Education Field life science have the highest attrition rate, while employees with human resources education have the lowest attrition percentage (Graph 12).

Graph 13. Percentage of Attrition Based on Distance From Home

• Employees with a Distance From Home of 1–5 have the highest attrition rate, whereas employees with a distance from home of > 25 have the lowest attrition rate (Graph 13).

Model Insights

• The algorithms used to build the predictive model of employee attrition in company ‘X’ are Logistic Regression, K-Nearest Neighbors Classifier, Decision Tree Classifier, Random Forest Classifier, XG Boost Classifier, dan Ada Boost Classifier.
• The metrics used to evaluate the model are Accuracy, Precision, Recall, and F-1 Score.
• Because we don’t want a large False Negative rate (prediction: retain, actual: attrition), the best algorithm is chosen based on the highest value of the Recall metric.
• According to the model evaluation results, the Logistics Regression model with a Recall-Score of 0.83 is the best classification model for predicting attrition.
• The results of the Logistic Regression model hyperparameter tuning were not different from the results before the hyperparameter tuning. This may be due to the small size of the dataset, possibly cause the results to be insignificant.

Graph 14. Feature Important

• Overtime is the most influential feature on attrition, followed by years since last promotion, marital status, number of companies worked for, and department. Monthly income has the least of an impact on attrition (Graph 14).

Business Recommendation

Because over time is the most influential factor on employee attrition at Company X, handling over time is a logical step that the company can take to reduce employee attrition. Simulation can be used to determine whether or not handling over time can reduce attrition. Here, the simulation that will be carried out is to reduce the level of employee over time up to 20%. The simulation will be applied to 20% of employees in this company. Before to the simulation, the employee attrition rate was 28.6 percent (Graph 15), and after the simulation was implemented, it was 25.9 percent (Graph 16).

Graph 15. Attrition Rate Before Simulation

Graph 16. Attrition Rate After Simulation

Source code of this project can be found here

Reference:

Staff Attrition vs. Staff Turnover: What’s the Difference? — jobZology

Attrition Rate: Definition, Calculator & What It’s Telling You (personio.com)