Feature engineering

HR Analytics: Predicting Employee Churn in R

Abhishek Trehan

People Analytics Practitioner

Feature engineering

  • Basic variables: Set of variables available directly in a dataset

  • Derived variables: Set of variables derived using data transformation of basic variables

HR Analytics: Predicting Employee Churn in R

Creating new features

  • Age difference between an employee and their manager
  • Job-hop index
  • Employee tenure
HR Analytics: Predicting Employee Churn in R

Age difference

  • Views
  • Handling pressure
  • Expectations
  • Work ethics
HR Analytics: Predicting Employee Churn in R

Job-hopping

$$ \text{Job-hop index} = \frac{\text{Total experience}}{\text{Number of companies worked}} $$

HR Analytics: Predicting Employee Churn in R

Employee tenure

  • Tenure: duration of employment

  • Inactive employees tenure

     date_joining & last_working_date
  • Active employees tenure
      date_joining & cutoff_date
HR Analytics: Predicting Employee Churn in R

Deriving employee tenure

# Coercing date variables from dd/mm/yyyy format 

library(lubridate)

org_final %>% 
  mutate(date_of_joining = dmy(date_of_joining), 
         cutoff_date = dmy(cutoff_date), 
         last_working_date = dmy(last_working_date))
HR Analytics: Predicting Employee Churn in R

Calculating timespan

# Computing time span in years

library(lubridate)

date_1 <- ymd("2000-01-01")
date_2 <- ymd("2014-08-09")

time_length(interval(date_1, date_2), "years")

14.60274
HR Analytics: Predicting Employee Churn in R

Let's practice!

HR Analytics: Predicting Employee Churn in R

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