Human Resources Analysis
Mohamed Gad
2024-07-04
Project Intro
A large company named XYZ, employs, at any given point of time, around 4000 employees. However, every year, around 15% of its employees leave the company and need to be replaced with the talent pool available in the job market. The management believes that this level of attrition (employees leaving, either on their own or because they got fired) is bad for the company, because of the following reasons -
1- The former employees’ projects get delayed, which makes it difficult to meet timelines, resulting in a reputation loss among consumers and partners
2- A sizeable department has to be maintained, for the purposes of recruiting new talent
3- More often than not, the new employees have to be trained for the job and/or given time to acclimatise themselves to the company
Hence, the management has contracted an HR analytics firm to understand what factors they should focus on, in order to curb attrition. In other words, they want to know what changes they should make to their workplace, in order to get most of their employees to stay. Also, they want to know which of these variables is most important and needs to be addressed right away.
Goal
I am required to model the probability of attrition using machine Learning. The results thus obtained will be used by the management to understand what changes they should make to their workplace, in order to get most of their employees to stay.
str(data)## 'data.frame': 4410 obs. of 29 variables:
## $ EmployeeID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 51 31 32 38 32 46 28 29 31 25 ...
## $ Attrition : chr "No" "Yes" "No" "No" ...
## $ BusinessTravel : chr "Travel_Rarely" "Travel_Frequently" "Travel_Frequently" "Non-Travel" ...
## $ Department : chr "Sales" "Research & Development" "Research & Development" "Research & Development" ...
## $ DistanceFromHome : int 6 10 17 2 10 8 11 18 1 7 ...
## $ Education : int 2 1 4 5 1 3 2 3 3 4 ...
## $ EducationField : chr "Life Sciences" "Life Sciences" "Other" "Life Sciences" ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ JobLevel : int 1 1 4 3 1 4 2 2 3 4 ...
## $ JobRole : chr "Healthcare Representative" "Research Scientist" "Sales Executive" "Human Resources" ...
## $ MaritalStatus : chr "Married" "Single" "Married" "Married" ...
## $ MonthlyIncome : int 131160 41890 193280 83210 23420 40710 58130 31430 20440 134640 ...
## $ NumCompaniesWorked : int 1 0 1 3 4 3 2 2 0 1 ...
## $ Over18 : chr "Y" "Y" "Y" "Y" ...
## $ PercentSalaryHike : int 11 23 15 11 12 13 20 22 21 13 ...
## $ StandardHours : int 8 8 8 8 8 8 8 8 8 8 ...
## $ StockOptionLevel : int 0 1 3 3 2 0 1 3 0 1 ...
## $ TotalWorkingYears : int 1 6 5 13 9 28 5 10 10 6 ...
## $ TrainingTimesLastYear : int 6 3 2 5 2 5 2 2 2 2 ...
## $ YearsAtCompany : int 1 5 5 8 6 7 0 0 9 6 ...
## $ YearsSinceLastPromotion: int 0 1 0 7 0 7 0 0 7 1 ...
## $ YearsWithCurrManager : int 0 4 3 5 4 7 0 0 8 5 ...
## $ EnvironmentSatisfaction: int 3 3 2 4 4 3 1 1 2 2 ...
## $ JobSatisfaction : int 4 2 2 4 1 2 3 2 4 1 ...
## $ WorkLifeBalance : int 2 4 1 3 3 2 1 3 3 3 ...
## $ JobInvolvement : int 3 2 3 2 3 3 3 3 3 3 ...
## $ PerformanceRating : int 3 4 3 3 3 3 4 4 4 3 ...Data Cleaning
Below, I will performing some data re-classifications for improve the analysis.
head(data)## EmployeeID Age Attrition BusinessTravel Department
## 1 1 51 No Travel_Rarely Sales
## 2 2 31 Yes Travel_Frequently Research & Development
## 3 3 32 No Travel_Frequently Research & Development
## 4 4 38 No Non-Travel Research & Development
## 5 5 32 No Travel_Rarely Research & Development
## 6 6 46 No Travel_Rarely Research & Development
## DistanceFromHome Education EducationField EmployeeCount Gender JobLevel
## 1 6 2 Life Sciences 1 Female 1
## 2 10 1 Life Sciences 1 Female 1
## 3 17 4 Other 1 Male 4
## 4 2 5 Life Sciences 1 Male 3
## 5 10 1 Medical 1 Male 1
## 6 8 3 Life Sciences 1 Female 4
## JobRole MaritalStatus MonthlyIncome NumCompaniesWorked
## 1 Healthcare Representative Married 131160 1
## 2 Research Scientist Single 41890 0
## 3 Sales Executive Married 193280 1
## 4 Human Resources Married 83210 3
## 5 Sales Executive Single 23420 4
## 6 Research Director Married 40710 3
## Over18 PercentSalaryHike StandardHours StockOptionLevel TotalWorkingYears
## 1 Y 11 8 0 1
## 2 Y 23 8 1 6
## 3 Y 15 8 3 5
## 4 Y 11 8 3 13
## 5 Y 12 8 2 9
## 6 Y 13 8 0 28
## TrainingTimesLastYear YearsAtCompany YearsSinceLastPromotion
## 1 6 1 0
## 2 3 5 1
## 3 2 5 0
## 4 5 8 7
## 5 2 6 0
## 6 5 7 7
## YearsWithCurrManager EnvironmentSatisfaction JobSatisfaction WorkLifeBalance
## 1 0 3 4 2
## 2 4 3 2 4
## 3 3 2 2 1
## 4 5 4 4 3
## 5 4 4 1 3
## 6 7 3 2 2
## JobInvolvement PerformanceRating EducationName1 EnvironmentSatisfaction1
## 1 3 3 College High
## 2 2 4 Below College High
## 3 3 3 Master Medium
## 4 2 3 PhD Very High
## 5 3 3 Below College Very High
## 6 3 3 Bachelor High
## JobInvolvement1 JobSatisfaction1 PerformanceRating1 WorkLifeBalance1 AgeGroup
## 1 High Very High Excellent Good 50 - 59
## 2 Medium Medium Outstanding Best 30 - 39
## 3 High Medium Excellent Bad 30 - 39
## 4 Medium Very High Excellent Better 30 - 39
## 5 High Low Excellent Better 30 - 39
## 6 High Medium Excellent Good 40 - 49Analysis
In this section, I will filter the data to display only those employees who are no longer with XYZ. I will then analyze various trends to understand the potential reasons why these employees chose to leave the company instead of continuing to grow within it.
## Attrition Percetange= 16.12 %## The highest two groups combined make up 71.73 % of the overall attritionAs shown in the graph, the age groups with the highest attrition rates are 30 to 39 and 20 to 29. This indicates that the most active age groups, which together make up nearly half of the overall workforce in the USA, are leaving the company. This trend is quite alarming as it suggests that a significant portion of XYZ’s most dynamic and potentially high-performing employees are choosing to leave rather than grow within the company.
As shown in the graph, the Research and Development department is experiencing the highest employee attrition. This high turnover could negatively impact XYZ’s productivity, as a lack of stable staffing in this crucial department may lead to instability in the company’s overall performance.
Education Level and Attrition by Department
Observation: The graph indicates that while the HR department shows a slightly higher average education level compared to other departments, the overall education levels across all departments round to approximately 3. This value corresponds to a Bachelor’s degree.
Interpretation: This suggests that attrition does not significantly vary with education levels, as the majority of employees in each department hold a Bachelor’s degree. Therefore, based on this data, education level does not appear to be a key factor influencing attrition rates.
As shown in the graph above, the top three job titles account for the largest number of employees experiencing attrition. Therefore, we will delve deeper into the data to closely examine these top three job roles with the lowest retention rates.
## `summarise()` has grouped output by 'JobRole'. You can override using the
## `.groups` argument.## The top 3 job titles related to attrition is 62.67 %Given that the top three job titles account for 62.67 % of the total attrition, we will delve deeper to analyze and understand the reasons and patterns behind this attrition.
Since the majority of attrition is occurring within the ‘Research & Development’ department, we will run some comparisons between the overall data and the new dataset containing only the attrition data related to the Research & Development department.”
Initial Findings
1- When comparing the average salaries between the entire company and the employees who left the Research & Development department, we found that the latter group’s average salary is 6.37 % below the company average. This indicates potential underpayment, which may contribute to these employees feeling undervalued and unfairly treated.
2- When comparing job satisfaction levels between the entire company and the employees who left the Research & Development department, we found that the latter group’s job satisfaction is 10.26 % lower than the company average. This significant decrease in job satisfaction suggests that these employees felt less content with their work environment, which may have contributed to their decision to leave. Addressing the factors leading to lower job satisfaction in this department could be crucial in reducing attrition rates.
3- The data indicates that employees who left the company worked with the same manager for an average of 3 years, compared to the company-wide average of 4.12 years, showing a difference of 27.18 % less. Additionally, these employees spent 20.74 % less time at the company overall. This suggests potential management issues within this department, which could be contributing to higher attrition rates.
4- The table below focuses on the Research & Development Department, which has a very high turnover rate. It shows that the number of employees who left the company is nearly 25% of those who stayed after reaching the three-year mark.
Validations Through Statistics
Feature Engineering
Below, I will proceed with feature engineering that would help me later on to apply it on Machine Learning models.
##
## Call:
## glm(formula = AttritionCode ~ Department + YearsWithCurrManager +
## Satisfaction, family = "binomial", data = train_data)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.33751 0.23673 1.426 0.153951
## DepartmentResearch & Development -0.60574 0.21802 -2.778 0.005463 **
## DepartmentSales -0.74332 0.23020 -3.229 0.001242 **
## YearsWithCurrManager1 -0.81284 0.23889 -3.403 0.000668 ***
## YearsWithCurrManager2 -1.06620 0.14685 -7.260 3.86e-13 ***
## YearsWithCurrManager3 -1.24640 0.20429 -6.101 1.05e-09 ***
## YearsWithCurrManager4 -1.28791 0.23784 -5.415 6.13e-08 ***
## YearsWithCurrManager5 -1.19616 0.39513 -3.027 0.002468 **
## YearsWithCurrManager6 -0.86738 0.37963 -2.285 0.022326 *
## YearsWithCurrManager7 -0.99281 0.16380 -6.061 1.35e-09 ***
## YearsWithCurrManager8 -1.33553 0.23626 -5.653 1.58e-08 ***
## YearsWithCurrManager9 -1.35209 0.29865 -4.527 5.97e-06 ***
## YearsWithCurrManager10 -1.15667 0.39359 -2.939 0.003295 **
## YearsWithCurrManager11 -3.01380 1.01688 -2.964 0.003039 **
## YearsWithCurrManager12 -15.86262 389.69866 -0.041 0.967531
## YearsWithCurrManager13 -15.85417 440.19133 -0.036 0.971269
## YearsWithCurrManager14 0.06416 0.71916 0.089 0.928913
## YearsWithCurrManager15 -15.57760 758.52748 -0.021 0.983615
## YearsWithCurrManager16 -16.23053 1199.24568 -0.014 0.989202
## YearsWithCurrManager17 -15.99342 792.07041 -0.020 0.983890
## Satisfaction -0.68018 0.11823 -5.753 8.76e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2656.4 on 3072 degrees of freedom
## Residual deviance: 2470.7 on 3052 degrees of freedom
## AIC: 2512.7
##
## Number of Fisher Scoring iterations: 15## Accuracy: 82.08## If attrition = Yes then 1 else 0## If attrition = Yes then 1 else 0## If attrition = Yes then 1 else 0- The Logistic Regression model corroborates the initial findings with an accuracy of 82.08 %.
- We will proceed with another machine learning model, specifically a Decision Tree, to seek higher accuracy %
## Decision Tree Model Accuracy: 82.84- The decision tree corroborates the initial findings with an accuracy of 82.84 %.
- We will proceed with another machine learning model, specifically a Random Forest, to seek higher accuracy %
## AttritionCode Department JobRole
## 0 0 0
## Education Gender Income
## 0 0 0
## YearsAtCompany YearsSinceLastPromotion YearsWithCurrManager
## 0 0 0
## Satisfaction PerformanceRating
## 0 0##
## 0 1
## 2595 478##
## 0 1
## 1085 232## Random Forest Model Accuracy: 89.22- The random forest model corroborated the initial findings, achieving an accuracy of 89.22 %. The variable importance plot indicates that Income and YearsAtCompany are the most significant predictors of attrition, highlighting the importance of compensation and tenure. Managerial relationships (YearsWithCurrManager) and career progression (YearsSinceLastPromotion) also play critical roles in predicting employee attrition.
Analysis Recommendations
1- The management in the Research and Development department should undergo refresher leadership training and re-evaluation.
2- XYZ should aim to reduce the income gap and ensure that no job title or department is significantly underpaid or overpaid, aligning salaries with the overall company average.
3- Implement a policy to promote employees at least once every 2.5 years from their start date, as most attrition occurs around the three-year mark.
4- By addressing these recommendations, overall job satisfaction is likely to increase, which will enhance employee retention and optimize workforce stability.