The document discusses a study on human resource analytics focused on predicting employee turnover using various factors such as satisfaction levels, years at the company, and workload. It details data preprocessing, modeling techniques, and findings, indicating that employees with low satisfaction and high project assignment are likely to leave. The study concluded that decision trees outperformed other models in predicting turnover, highlighting key predictors of employee retention.

1. Human Resource Analytics
“A company is only as good as the people it keeps”
OPIM 5604 Predictive Modeling
Apoorva Srivastava| Jin Zhao| Saurav Gupta| Yiming Liang| Vibhuti Upadhyay| Yulei Xia

2. Introduction
• 3.22 million Americans quit in Jan 2017
• Replacing entry level employee costs 50 percent of persons salary
• Replacing higher level employee costs 250 percent of persons salary
• Intellectual and monetary loss for the company
WHY?

3. Objective
“Whether an employee will leave or not?”

4. Original Dataset
Data Preprocessing

5. Variable
Name
Satisfaction
Level
Last
Evaluation
Number of
Projects
Average
Monthly
Hours
Years at the
Company
Work
Accidents?
Promotion
in the Last 5
Years?
Department Salary Left?
Data Type Numeric Numeric Numeric Numeric Numeric Character Character Character Character Character
Modeling
Type
Continuous Continuous Continuous Continuous Continuous Nominal Nominal Nominal Ordinal Nominal
Data Preprocessing
Variable Name, Data Type & Modeling Type

6. Data Preprocessing
Missing Values & Outliers

7. 7142 rows left for future modeling process
Left? Number of Rows
1 3571
0 3571
Data Preprocessing
Stratification

8. No Promotion 😕 ! More Projects More Work LEFT
Data Visualization

9. Who has spent 4-5 years at the
company LEFT
Data Visualization
Also these people are the one who
work more than 250 hours at work

10. People who have
• Low And Medium salary
• Low satisfaction level
• Work more average hours
LEFT
Data Visualization

11. Most people who left were part of HR and
Accounting 😒
People in R&D, Product Mgmt., and Marketing
are amongst the least likely to leave.
Most promotions happens in R&D,
Management, and Marketing.
Data Visualization | Department Trends

12. Marketing, R&D & Product Mgmt. Accounting, Tech and Support
Turnover Likely to stay Likely to leave
Satisfaction
Most satisfied Not satisfied
Performance Evaluation
Low High
Hours worked
Amongst lowest Amongst highest
Promotions
Most promoted Least promoted
Discovery Based on Departments
Firm depends on Marketing and
R&D i.e. why these folks tend to
get promoted more easily
• Hardest working people.
• Higher performance evaluations
• But amongst the least satisfied,
and least promoted
• Work less as far as hours
• Most satisfied
• Most promoted

13. Working > 250 hours
per month
Common Traits of People Leaving

14. • Here the Y variable is Left?
• Goal : To predict which employee will leave
• Setup of Modelling
• Estimate the probabilities - in a binary case (1/0)
• Cut off Value = 0.5
• P (Y = 1) > 0.5 are classified as belonging to class 1,
whereas
• P (Y = 1) < 0.5 are classified as belonging to class 0.
Predictive Modeling

15. With All Predictors Without All Predictors
Misclassification
AICC
BIC
RMSE
• We will keep the model will all the predictors.
Logistic Regression

16. 😧 😣Not that Great
ROC & Lift Curves
Logistic Regression

17. Partition Modeling

18. From the classification tree, the predicted class of people who will leave is
2038. The actual class of people who will leave is 2187. The error rate of
Left is 0.5%.
Fit Details
Partition Modeling

19. The column contributions reveal the most principal factors in the
model. Satisfaction level, years at the company, number of projects,
average monthly hours and last evaluation contribute 100% of the
model.
Column Contributions
Partition Modeling

20. ROC & Lift Curves
Partition Modeling

21. If the satisfaction level is above the median and the years at the company is
short, the employee will most likely not to leave.
If the satisfaction level is below the median but the number of projects
assigned is large, the employee will most likely leaving.
If the satisfaction level is above the median for some employees, if they are
not assigned a lot of projects, they will leave to seek more opportunities.
Leaf Report
Partition Modeling

22. Neural Nets

23. Neural Nets

24. Neural Nets

25. ROC & Lift Curves
Neural Nets

26. Performance Evaluation
Classification Matrix

27. Performance Evaluation
ROC and Lift Curves for Validation and Test

28. From above comparisons, we can see that on Validation and Test dataset the Decision
Trees outperforms all other models.
∙ Good R^2 values
∙ Least Root Mean Square Error
∙ Lower Misclassification Rate
z
Performance Evaluation

29. Conclusion
Satisfaction level Years of experience
Number of projects