![Employee Churn Prediction
• [1]. P. Ajit, "Prediction of Employee Turnover in Organizations using Machine Learning
Algorithms".
• [2]. M. Guirao, "Predicting Employee Attrition with Machine Learning".
• [3]. F. Fallucchi, E. W. D. Luca and R. Giuliano, "Predicting Employee Attribution Using
Machine Learning"..
• [4]. Qutub, Aseel, et al. ”Prediction of Employee Attrition Using Machine Learning and
Ensemble Methods.” Int. J.
• Mach. Learn. Comput 11 (2021)
Reference :](https://image.slidesharecdn.com/harishppt-240609175739-1b975f07/85/Artificial-intelegence-for-internship-student-AI-pptx-17-320.jpg)
Artificial intelegence for internship student (AI) .pptx
- 1. Face Emotion and Age Detection Student Details EMPLOYEE CHURN PREDICTION Name : N.Harish NM Id:: aut21lee08 College Name: RVS College of Engineering
- 2. Employee Churn Prediction Course Outline • Business case • Data exploration and preparation • Split data into training and validation • Develop an initial model and interpret two complete paths • Identify important variables • Summary • GitHub Link • Future scope • Conclusion • References
- 3. Employee Churn Prediction In this case study, we will visualize two paths of attributes that affect loyalty and dissatisfaction among employees. The business case is formed around the question: Can we predict those employees who are likely to churn? Business case :
- 4. Employee Churn Prediction • There are eight continuous variables and two categorical variables in the data set that offers information about 14999 employees. Continuous variables are those with numerical values, and categorical variables group things into category headers, like “Departments” that can have values similar to sales, marketing, consumer, operations, and so on Data exploration and preparation :
- 5. Employee Churn Prediction 1.satisfaction_level: Satisfaction ratings of the job of an employee 2.last_evaluation: Rating between 0 to 1, received by an employer over their job performance during the last evaluation 3.number_projects: Number of projects an employee is involved in 4.average_monthly_hours: The average number of hours in a month, spent by an employee at the office 5.time spent_company: Number of years spent in the company 6.work_accident: 0-no accident during employee stay, 1 accident during employee stay 7.promotion_last 5 years: Number of promotions in the employee’s stay period 8.resigned: 0 indicates the employee stays in the company, 1 indicates-the employee who resigned from the company 9.salary_grade: Salary earned by an employee 10.department: the department to which an employee belongs The variables are explained in the data dictionary below:
- 6. Employee Churn Prediction • We will split the data into two parts: training and validation but let’s understand why we do that. We train humans to perform a skill. Similarly, we can train the algorithm to perform. To train a human, we let them practice towards perfecting their ability. But for algorithms, we input data so that they can learn. • The algorithm identifies the pattern in the data and learns the intricacies and nuances of that pattern to build an ability to predict accurately. Therefore, we split our dataset so that we can test the trained model on a representative dataset where we already know the correct predictions. This will let us know how well the model that we trained is performing. • But before we train the model, we will create factors of the following variables Split data into training and validation :
- 7. Employee Churn Prediction • Department: Represents the number of employees in each department. There are a total of 10 departments. Department Sales has the highest number of employees at 27% and management the lowest which forms only 4.2%. • Salary grade: Represents the salary as low medium and high. 8.25% of the organization are top level with the highest pay, 42.9% of the employees are paid a medium salary and 48.7% of the employees are paid a low salary. • Resigned: In this, 0 denotes who stayed and 1 denotes who resigned from the organization. • We create factors when we wish that each type within a variable be treated as a category. For example, in R’s memory, factorizing the variable ‘department’ will mean treating, ‘low,’ ‘high,’ and ‘medium’ as individual categories. This ensures that the modeling functions treat each type correctly.
- 8. Employee Churn Prediction Develop an initial model : The initial model is developed on the training data set
- 9. Employee Churn Prediction How to read the tree ? • 1 denotes ‘resigned,’ and 0 denotes ‘stayed’ • At the top when no condition is applied to the training data set (train) the best guess is determined as 0 (stayed) • Of the total observations 76% did not leave and 24% left
- 10. Employee Churn Prediction Interpreting two complete paths : Path 1: Will not leave (Loyal) • first condition: satisfaction level >= 47% • second condition: time_spend_company < 5 years • third condition: last_evaluation < 81% • Hence, those who did NOT leave are highly satisfied, have spent at least 4 years in the organization, and are good performers with an evaluation of at least 80%. Path 2: Will leave (Resign) • first condition: satisfaction_level < 47% • second condition: number_project >= 3 projects • third condition: last_evaluation >= 58% • Hence, those who leave are lowly or moderately satisfied and have a workload of 3 or more projects with their performance being evaluated at least 58%.
- 11. Employee Churn Prediction Identify the important variables :
- 12. Employee Churn Prediction Summary : Characterizing loyalty 11,428 employees, which is, 76% of the data set are loyal. Three conditions that affect loyalty are: • a high level of satisfaction (satisfaction_level >= 47%) • have spent at least 4 years in the organization (time_spend_company < 5 years) • are good performers with an evaluation of at least 80% (last_evaluation < 81%)
- 13. Employee Churn Prediction Characterizing left : 3,571 employees, which is, 24% of the data set left. Three conditions that affect ‘resigned’ are: • low or moderate satisfaction (satisfaction_level < 47%) • have a workload of 3 or more projects (number_project >= 3 projects) and • their performance being evaluated at least 58% (last_evaluation >= 58 %)
- 14. Employee Churn Prediction Future scope : • Job satisfaction is the most important factor in predicting attrition. • Employees who are satisfied with their jobs are less likely to leave. • Based on the data used, focusing on these top 5 features the company can reduce employee attrition and improve its bottom line.
- 15. Employee Churn Prediction Conclusion : • Employees are the main asset to the company and the company can’t run without them • They play an important role in shaping the company and sending the company to next heights i.e making it successful hence the algorithm proposed by the authors help the HR managers to know what should be improved if an employee is thinking of quitting or finding a new job and by this they would not loose a credible employee to any other rival company • So the accuracy given by the algorithm would make an impact on the present systems and increase the predictions by a very high percentage,
- 16. Employee Churn Prediction GitHub Link :
- 17. Employee Churn Prediction • [1]. P. Ajit, "Prediction of Employee Turnover in Organizations using Machine Learning Algorithms". • [2]. M. Guirao, "Predicting Employee Attrition with Machine Learning". • [3]. F. Fallucchi, E. W. D. Luca and R. Giuliano, "Predicting Employee Attribution Using Machine Learning".. • [4]. Qutub, Aseel, et al. ”Prediction of Employee Attrition Using Machine Learning and Ensemble Methods.” Int. J. • Mach. Learn. Comput 11 (2021) Reference :
- 18. Employee Churn Prediction Thank you