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Course

HR Analytics: Predicting Employee Churn in R

IntermediateSkill Level

4.8+

Updated 08/2024

Predict employee turnover and design retention strategies.

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RMachine Learning4 hr14 videos50 Exercises4,000 XP4,833Statement of Accomplishment

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Course Description

Organizational growth largely depends on staff retention. Losing employees frequently impacts the morale of the organization and hiring new employees is more expensive than retaining existing ones. Good news is that organizations can increase employee retention using data-driven intervention strategies. This course focuses on data acquisition from multiple HR sources, exploring and deriving new features, building and validating a logistic regression model, and finally, show how to calculate ROI for a potential retention strategy.

Prerequisites

HR Analytics: Exploring Employee Data in R

1

Introduction

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.

What is turnover?

Objectives of employee turnover prediction

Importing headcount and turnover data

Exploring the data

What proportion of employees have left?

Which levels have high turnover rate?

Is turnover rate different across locations?

HR data architecture

Filtering the dataset

Combining HR datasets (I)

Combining HR datasets (II)

Master data overview

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).

Feature engineering

Derive age difference

Derive job hop index

Derive employee tenure

Compensation

Exploring compensation

Deriving Compa-ratio

Deriving Compa-level

Information value

Calculating Information Value

Which variables are important?

3

Predicting Turnover

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

Data splitting

Split the data

Corroborate the splits

Introduction to logistic regression

Build your first logistic regression model

Build a multiple logistic regression model

Interpreting significance levels

Multicollinearity

Detecting multicollinearity

Dealing with multicollinearity

Building your final model

Building final logistic regression model

Understanding the model predictions

Interpret the results

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.

Validating logistic regression results

Create a confusion matrix

Accuracy of your model

Designing retention strategy

Calculate turnover risk probability

Creating turnover risk buckets

What would you do?

Return on investment

Create salary hike range

Calculate turnover rate across salary hike range

Calculate ROI

HR Analytics: Predicting Employee Churn in R

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FAQs

What predictive model is used to forecast employee churn in this course?

The course builds a logistic regression model to predict employee turnover, accounting for multicollinearity among variables and validating accuracy with real HR data.

Does this course teach how to calculate the ROI of a retention strategy?

Yes, the final chapter shows you how to categorize employees into risk buckets, formulate an intervention strategy, and calculate the return on investment for that strategy.

What is compa-ratio and why is it used in this course?

Compa-ratio measures pay equity by comparing an employee's salary to a benchmark. You create this feature in Chapter 2 as part of engineering variables that explain turnover.

Is this course suitable for someone new to HR analytics in R?

No, this is an advanced course. You should first complete HR Analytics: Exploring Employee Data in R, along with prerequisites in regression, statistics, dplyr, and ggplot2.

How does the course handle data from multiple HR sources?

Chapter 1 teaches you to acquire and combine data from multiple HR data sources, identify relevant talent segments, and calculate turnover rates across different dimensions.

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