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  3. HR Analytics: Predicting Employee Churn in R
  • 1

    Introduction

    Free

    This chapter begins with a general introduction to employee churn/turnover and reasons for turnover as shared by employees. You will learn how to calculate turnover rate and explore turnover rate across different dimensions. You will also identify talent segments for your analysis and bring together relevant data from multiple HR data sources to derive more useful insights.

  • 2

    Feature Engineering

    In this chapter, you will create new variables from existing data to explain employee turnover. You will analyze compensation data and create compa-ratio to measure pay equity of all employees. To identify the most important variables influencing turnover, you will use the concept of Information Value (IV).

  • 3

    Predicting Turnover

    In this chapter, you will build a logistic regression model to predict turnover by taking into account multicollinearity among variables.

  • 4

    Model Validation, HR Interventions, and ROI

    In this chapter, you will calculate the accuracy of your model and categorize employees into specific risk buckets. You will then formulate an intervention strategy and calculate the ROI for this strategy.

Initializing

Exercise

Importing headcount and turnover data

Since employee information is confidential, you will be working with data that was artificially generated (keeping the distribution as close as possible to real-world data) for learning purposes.

In this exercise, you will work with the org dataset which contains information for all employees who were working or who left the organization during the year 2014.

Instructions

100 XP
  • Load the readr and dplyr packages.
  • Import 'org.csv' using read_csv().
  • Use glimpse() to view the structure of org.