1. Employee Turnover: Data Analysis and Exploration
Group 7: Bashe Aden, Brian Keenum, Gordon Kelly
BIT Spring 2018

2. Business Problem and Questions being Investigated
Most valuable employees are leaving the company
Turnover is expensive in terms of recruiting and training costs
Could potentially increase retention and reduce costs with better understanding of this phenomenon
Questions Being Investigated:
Why employees are leaving, and in what timeframe
Looking at attributes of employees, perhaps we can predict the employees that will leave next.
Adjust work environment so that these employees will stay with the firm

3. Description of Data Sources and Preliminary Work
Dataset is called Human Resource Analytics
Available for download at Kaggle: analytics
9 independent variables, dependent target variable is “employee left”
15,000 observations
CSV format
Data dictionary created (as seen here)
Assessed any formatting errors (none found)
Addressed null values (none found)
Variable Dictionary
Variable Name
Description
Datatype
satisfaction_level
Employee satisfaction level
continuous
last_evaluation
Employee evaluation score
number_project
Number of employee projects
average_montly_hours
Average monthly hours per employee
time_spend_company
Time employee spent at company in years
Work_accident
Whether employee has had a work accident (1 = Yes, 0 = No)
Nominal
left
Whether the employee has left the company (1 = Yes, 0 = No)
promotion_last_5years
Whether employee has been promoted within last 5 years (1 = Yes, 0 = No)
sales
Sales type
salary
Employee salary level
Ordinal

4. Planned Analysis Logistic Regression Principal Components Analysis
Show potential two-way relationships between predictor variable and exploratory variables
Appropriate since the dependent variable is binary
Independent variables are a mix of nominal, continuous and ordinal data types
Principal Components Analysis
Creates more efficient model, should increase accuracy and reduction of errors through removal of less correlated variables.
Using the 80% rule, making sure that we keep the components that account for 80% of the variation in the data set
Classification Tree
To uncover potential relationships that may have been missed by the logistic regression
Allow for future prediction of whether or not an employee will leave

5. Logistic Regression
We initiated this exploratory analysis by first examining each independent variable one at a time against the variable of interest
First, by Satisfaction level,
you can see a strong correlation
that as satisfaction level increases
the instances of the employee
leaving decreases
As you can see in this
Contingency analysis
Of left by salary, the
Higher the salary the lower
The instance of leaving
Only 6.6% of employees
With a high salary leave,
Compared to 29.7% of those
With low salaries leaving

6. Logistic Regression- Continued
Finally, looking at the two way relationship between “left” and time_spend_company, it is clear that there is a positive trend in this relationship. The more time that the employee spends at the company the more likely they are to leave.
Management decisions can be inferred from this, could this be burnout of employees?

7. Principal Component Analysis
Produced correlations and summary plot
Satisfaction and salary are high positive contributors to staying in the organization
Eigenvalues reveal that satisfaction, last evaluation and number of projects have a higher proportion of effect
Top 6 components chosen to use for the principal components, as the total variation represented by these is 85.25%

8. Principal Component Analysis- Continued
Regression results for Prin1, Prin2 show an R2 Value of .1234, with an RMSE value of .3988
Prin1 results in a R2 of and RMSE of .4237
principal components yielded an R2 of and an RMSE of .3854
This shows an improvement in R2 value and a reduction in error over the prin1, prin2 components
Model R2
RMSE
Prin1, Prin2
.1234
.3988
Prin1
.0103
.4237
Principal Components
.1813
.3854

9. Comparison of Models
Logistic Regression and PCA show some differences and some similarities
Both show that satisfaction level, salary and time spent are significant contributors to whether or not an employee leaves the company
PCA also adds number of hours and number of projects to factors
Analysis by second model shows 77% accuracy
First model shows through the contingency table that nearly 30% of those with low salary leave, with only 6% of high salaried employees leaving the company.

10. Classification Tree
Purpose to uncover relationships that may have been missed by the other two models
Target variable is “left” and the independent variables are
Satisfaction level
Last evaluation
Number of projects
Average monthly hours
Time spent with the company
Work accidents
Promotions within last 5 years
Sales
Salary
Tree was split 10 times

11. Classification Tree Continued
The entropy R2 for the training set was .79, and it was .78 for the validation set
RMSE values for training set were .160, and were .164 for the validation set

12. Classification Tree ROC Curve
Both training and validation sets show accurate response values
Nearly identical results for each data set
Crossing of response values show performance is better between yes and no depending on the cut off point

13. Summary Lower satisfaction levels at work leads to higher attrition
The more satisfied employees, who are also more experienced (worked longer at company), are leaving if they have high average_monthly_work_hours (spend a lot of time at work).
Employees with strong evaluations (good employees) are working long hours and leaving
Employees who are satisfied, but with fewer projects do not feel challenged?
After comparing the results of all the models this report concludes that employee satisfaction level, salary paid, hours worked, number of projects assigned, and time spend with the company are the main contributors of employee’s decision of leaving the company.