Every year a lot of companies hire a number of employees. The companies invest time and money in training those employees, not just this but there are training programs within the companies for their existing employees as well. The aim of these programs is to increase the effectiveness of their employees.
- But where HR Analytics fit in this?
- and is it just about improving the performance of employees?
| Column Name | Description |
|---|---|
AGE |
Numerical Value |
ATTRITION |
Employee leaving the Company (0=no, 1=yes) |
BUSINESS TRAVEL |
(1= No Travel, 2= Travel Frequency, 3= Travel rarely) |
JOB ROLE |
(1=HC REP, 2=HR, 3=LAB TECHNICIAN, 4=MANAGER, 5= MANAGING DIRECTOR, 6= REASEARCH DIRECTOR, 7= RESEARCH SCIENTIST, 8=SALES EXECUTIEVE, 9= SALES REPRESENTATIVE) |
MARITAL STATUS |
(1=DIVORCED, 2=MARRIED, 3=SINGLE) |
GENDER |
(1=FEMALE, 2=MALE) |
OVER 18 |
1=YES, 2=NO) |
OVERTIME |
(1=NO, 2=YES) |
EDUCATION FIELD |
(1=HR, 2=LIFE SCIENCES, 3=MARKETING, 4=MEDICAL SCIENCES, 5=OTHERS, 6= TEHCNICAL) |
DEPARTMENT |
(1=HR, 2=R&D, 3=Sales) |
| Description | Analysis |
|---|---|
| hr_data.head |  |
| dummies |  |
| final |  |
- We saw how we can avoid using correlated values and why it is important not to use those while modelling.
- We used Random forest and learned how it can be very advantageous over other available machine learning algorithm.
- Most of all we found factors which are most important to employees and if are not fulfilled might lead to Attrition.