Attrition control Project

1. TAKENMIND GLOBAL DATA
ANALYTICS INTERNSHIP
PROOF –OF- CONCEPT
NAME: MOHAMMAD SHAIZ AKHTAR
COUNTRY: INDIA
LINKEDIN: HTTPS://WWW.LINKEDIN.COM/IN/MOHAMMAD-SHAIZ-AKHTAR-5439B5169/
EMAIL: SHAIZMOHAMMAD@GMAIL.COM
PROJECT TITLE: ATTRITION CONTROL

2. PROBLEM STATEMENT
 The data is for company X which is trying to control attrition. There are
two sets of data: “Existing employees” and “Employees who have left”.
OBJECTIVE:
1. What type of employees are leaving?
2. Which employees are prone to leave next?

3. APPROACH
 Read the given excel sheet using pandas which has an in-built function to
read (.xlsx) files.
 Preprocess the data to check whether any of the worksheets have ‘NaN’
values.
 Once data is preprocessed, we will perform univariate, bi-variate and
multi-variate analysis to understand the behavior of features by plotting
on plots and graphs.

4.  Once we read the excel sheet, we find out that there are two worksheets present in the excel file, namely- ‘Existing’ and ‘Left’ employees of a company.
There are 10 columns in each table. They are:
1. EmpID
2. Satisfaction_level
3. Last_evaluation
4. Number_project
5. Average_monthly_hours
6. Time_spend_company
7. Work_accident
8. Promotion_last_5years
9. Dept
10. Salary
 Major features affecting an employee’s attrition:
1. Satisfaction
2. Promotion
3. Average Monthly Hours
4. Salary

5. UNIVARIATE ANALYSIS
 Satistisfaction
1. Employees who left: 40% satisfaction
2. Existing employees: 70% satisfaction
 Average Monthly Hours
1. Employees who left: 225 hrs
2. Existing employees: 200 hrs
 Salary
1. Employees who left: ‘Low’ salary employees domination is observed.
2. Existing employees: Roughly equal number of ‘Medium’ and ‘Low’ salary
employees is observed.

6. EXISTING LEFT

7. BIVARIATE ANALYSIS
 From the Box plot of both Existing Employees and those who Left, in terms
of number_project we can discover the following insights:-
 Employees who left - The median Satisfaction level of employees who
have done 5 projects is around 60% and after 4 projects satisfaction level
decreases.
 Existing employees - The median Satisfaction level of employees who
have done 4 projects is around 65% and after 4 projects satisfaction level
decreases.

8. EXISTING LEFT

9. MULTI-VARIATE ANALYSIS
 Satisfaction Level
 Employees who left - Satisfaction level of the employees who were not
promoted in the last 5 years was around 40% in all the departments
whereas satisfaction level varied for those employees who were promoted
in the last 5 years, also the employees were promoted in only few
departments(very few in sales, IT, management and negligible in technical,
support, management)
 Existing employees - Satisfaction level of the employees who are not
promoted in the last 5 years is around 70% in all the departments whereas
satisfaction level varies(but doesn't fall below 60%) for those employees
who are promoted in the last 5 years, also the employees are promoted in
all departments excluding IT and product management

10. EXISTING LEFT

11. PEOPLE WHO ARE PRONE TO LEAVE
 People having low satisfaction are more likely to leave.
 Higher average monthly working hours directly affects the attrition rate
of an employee causing employees to leave.
 If the number of project exceeds 3, the employees of the company are
more likely to leave.
 If the salary is low and the number of projects assigned to an employee
is larger, employee is more prone to leave.
 If average monthly hours is greater than 200 hrs and time spent is
more than 4 years, then there is a possibility of the employee to leave.

12. CONCLUSION
 In other to prevent future attrition and constantly maintain a high
satisfaction level of employees, the company should ensure the number of
projects allocated to an employee is not too overwhelming thereby losing
enthusiasm which may result to seeing a need to leave

13. Thank you TAKENMIND for this Internship for helping me improve my
Data Analytics Skills