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HR Analytics: Predicting Employee Churn in R

Predict employee turnover and design retention strategies.

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4 horas14 vídeos50 exercícios
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Descrição do Curso

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.
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  1. 1

    Introduction

    Gratuito

    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.

    Reproduzir Capítulo Agora
    What is turnover?
    50 xp
    Objectives of employee turnover prediction
    50 xp
    Importing headcount and turnover data
    100 xp
    Exploring the data
    50 xp
    What proportion of employees have left?
    100 xp
    Which levels have high turnover rate?
    100 xp
    Is turnover rate different across locations?
    100 xp
    HR data architecture
    50 xp
    Filtering the dataset
    100 xp
    Combining HR datasets (I)
    100 xp
    Combining HR datasets (II)
    100 xp
    Master data overview
    100 xp
  2. 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).

    Reproduzir Capítulo Agora
    Feature engineering
    50 xp
    Derive age difference
    100 xp
    Derive job hop index
    100 xp
    Derive employee tenure
    100 xp
    Compensation
    50 xp
    Exploring compensation
    100 xp
    Pay Gap
    50 xp
    Deriving Compa-ratio
    100 xp
    Deriving Compa-level
    100 xp
    Information value
    50 xp
    Calculating Information Value
    100 xp
    Which variables are important?
    50 xp
  3. 3

    Predicting Turnover

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

    Reproduzir Capítulo Agora
    Data splitting
    50 xp
    Split the data
    100 xp
    Corroborate the splits
    100 xp
    Introduction to logistic regression
    50 xp
    Build your first logistic regression model
    100 xp
    Build a multiple logistic regression model
    100 xp
    Interpreting significance levels
    50 xp
    Multicollinearity
    50 xp
    Detecting multicollinearity
    100 xp
    Dealing with multicollinearity
    100 xp
    Building your final model
    50 xp
    Building final logistic regression model
    100 xp
    Understanding the model predictions
    100 xp
    Interpret the results
    50 xp
  4. 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.

    Reproduzir Capítulo Agora
    Validating logistic regression results
    50 xp
    Create a confusion matrix
    100 xp
    Accuracy of your model
    100 xp
    Designing retention strategy
    50 xp
    Calculate turnover risk probability
    100 xp
    Creating turnover risk buckets
    100 xp
    What would you do?
    50 xp
    Return on investment
    50 xp
    Create salary hike range
    100 xp
    Calculate turnover rate across salary hike range
    100 xp
    Calculate ROI
    100 xp
    Wrap-up
    50 xp
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conjuntos de dados

Employee data

colaboradores

Collaborator's avatar
Sascha Mayr
Collaborator's avatar
Sumedh Panchadhar

pré-requisitos

HR Analytics: Exploring Employee Data in R
Abhishek Trehan HeadshotAbhishek Trehan

People Analytics Practitioner

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