Motivation: To Uncover the factors that leads to employee Attrition Goal: 1. To perform a data exploration in the data set by using SQL and R 2. Visualize the data using Tableau using interactive dashboard 3. Build a Random forest algorithm that could help us predict the factors leading to the employee attrition. Data: IBM’s Employee attrition data: The data is found in the below URL (Kaggle Repository) https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/data

1. IS6030
NAME: AYANK GUPTA UCID:M12388639
Background: IBM’s HR Analytics
Motivation: To Uncover the factors that leads to employee Attrition
Goal:
1. To perform a data exploration in the data set by using SQL and R
2. Visualize the data using Tableau using interactive dashboard
3. Build a Random forest algorithm that could help us predict the factors leading to the
employee attrition.
Data: IBM’s Employee attrition data:
The data is found in the below URL (Kaggle Repository)
https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset/data
Description on the data:
Contains Various employee Identifiers as Age, Gender,ID
And various metrices like length of stay in the company,Average Monthly Salary
In total it has around 37 columns for us to explore and make the data a little bit more
meaningful

2. PROJECT INDEX
➢ CHAPTER 1: DATA PREPARATION
➢ Performing the completeness check of each variable – examine if missing values are present;
➢ Performing the validity check of each variable – examine if abnormal values are present;
➢ Cleaning the data based on the results of Steps 2 and 3;
➢ Summarizing the distribution of each variable (what tables and figures will you present?)
➢ CHAPTER 2: Descriptive Study (XY plots and correlation studies)
➢ Studying the X-Y plot between the different variables.
➢ Performing Various data exploration analysis
➢ CHAPTER 3: Statistical Modelling
➢ Preparing a model to predict the relationship between the independent variable and the dependent
variables
➢ CHAPTER 4: Visualizing Using Tableau
➢ CHAPTER 5: Project Summary (report)

3. CHAPTER 1: DATA PREPARATION
➢ Data Explanation:
S.No Column Name Column Definition Data Type
1 Age Age of Employees Numeric
2 Attrition Employee still in company status Categorical
3 BusinessTravel Opportunity of Travel Categorical
4 DailyRate Daily rate Numeric
5 Department Employee's Department Categorical
6 DistanceFromHome Employee's Distance from home Categorical
7 Education Level Eductaion Categorical
8 EducationField Field of the education Categorical
10 EmployeeNumber Unique Employee Identifier Numeric
11 EnvironmentSatisfaction Factor for Employee Satisfaction Categorical
12 Gender Employee gender Categorical
13 HourlyRate HourlyRate Numeric
14 JobInvolvement Involvment in the Job Categorical
15 JobLevel Level of the Job Categorical
16 JobRole Role in the Job Categorical
17 JobSatisfaction Satisfaction score of the employee Numeric
18 MaritalStatus Married or Not Categorical
19 MonthlyIncome Monthly income Categorical
20 MonthlyRate Monthl Salary Numeric
21 NumCompaniesWorked
Number of companies worked
before Numeric
22 Over18 whether 18+ ? Categorical
23 OverTime whether used to work overtime Numeric
24 PercentSalaryHike % Salary Hike Categorical
25 PerformanceRating
Performanceo rating of the
Employee Numeric
26 RelationshipSatisfaction Relationship satisfaction rating Categorical
27 StandardHours Standard working hours Numeric
28 StockOptionLevel StockOptionLevel available ? Categorical
29 TotalWorkingYears # Workingyears Numeric
30 TrainingTimesLastYear # Trainings Numeric
31 WorkLifeBalance Work life balance Numeric
32 YearsAtCompany
# years wrking for the same
company Numeric
33 YearsInCurrentRole # Years in current role Numeric
34
YearsSinceLastPromotio
n # years since last year Numeric
35 YearsWithCurrManager # years with the current manager Numeric

4. ➢ Data Normalization:
Data is fine form , as it has all the required columns for analysis and prediction.
The data can be randomly divided into 2 data sets i.e Test and training data sets for the prediction
algorithm
➢ Data Cleaning:
1. Performing the completeness check of each variable
a. The whole data is unique at the Employee number level.
b. Are there, in any missing value ?
c. Bad columns
All the columns are aptly named , Except I had to make a age bucket columns
i.e above 30 and below 30 to have planned analysis on the age group.
Inconsistency in data types corrected:
I observed few of the data types were not consistent

5. ➢ Using SQL for genera statistics, data description and data manipulation
After loading in the excel file in SQL, lets try to do some basic statistics
We will finding the statistics of the below variables
1. YearsWithCurrManager
2. YearsSinceLastPromotion
3. YearsInCurrentRole
4. YearsAtCompany
5. WorkLifeBalance
6. PerformanceRating
7. MonthlyIncome

7. Note: As opposite to the popular belief female on an average gets paid more than males.
Note: Another shocker all the people below 30 earn more on an average that their experienced
counterpart
Now let’s move our analysis to R , Firstly we need to connect our sql data base in to R.
Now let’s check the structure of the data base

8. Finally lets check the the statistically summary of the data sets to check for any discrepancies if any

9. A few basic summaries
Lets look at few of the visualizations in R

10. Creating a Machine learning algorithm-Random Forest for prediction Employees Attrition
Now use the VarImplot function to find out the most important factors

11. As we can see a few important factors in predicting the attritionis OverTime, MonthlyIncome,Total
Working Income and Job Roles
And hence we can study these factors in detail to explore more about in detail in the tableau
dashboard

12. Learning about the insights by using Tableau dashboards.
I tried to make the dashboard completely interactive, so that even a common man could drive
insights through it.
Few of the observations:
1. Most of the Employees are from the Life Sciences closely followed by Medical and
Marketing.
a. Least number of employees belongs to the HR
2. ~16% of the Employees in general leave the company per year.
3. Employees above is 30 are more in number as compared to employees in less than 30.
a. Maximum Employees are mail above 30.
b. And Minimum employees are female 30
In the interactive big boxes above we can also look at various metrices that will be ultra helpful to
the HR like

13. 1. Avg Working hours of the selected employees
2. Avg years in the company
3. Average salary hike
4. Avg salary
Now we select the population that left company and we will be able to see a drastic change
And if we compare the above results with the people who have stayed in the company the
difference will be clear

15. Summary or the conclusion of the findings in the analysis
Below points will help uncover the reason why the employees left the company
1. The Average Salary of the employees who left was almost 33% less than the person who
stayed.
2. The Average Salary hike of the people that stayed in the office was marginally more that
people who left.
3. The Average Working years of the people who stayed were ~3 years more that people who
left
a. This means experienced people are reluctant to switch companies
4. Years with manager: On an average the people who stayed had more time with manager as
compared to the who left
Difficulties faced
1. The Assignment was at the time of other examinations so that to take out time in
completing the assignment
2. It was challenging but good to master Tableau as well.
3. Finding the dataset was also difficult.