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Datacamp

Human Resources Analytics: Predicting Employee Churn in R

via Datacamp

Overview

Predict employee turnover and design retention strategies.

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.

Syllabus

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.

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).

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

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.

Taught by

Anurag Gupta and Abhishek Trehan

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