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Human Resources Analytics: Predicting Employee Churn in Python

In this course you'll learn how to apply machine learning in the HR domain.

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4 Hours14 Videos44 Exercises6,686 Learners3500 XP

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Course Description

Among all of the business domains, HR is still the least disrupted. However, the latest developments in data collection and analysis tools and technologies allow for data driven decision-making in all dimensions, including HR. This course will provide a solid basis for dealing with employee data and developing a predictive model to analyze employee turnover.

  1. 1

    Introduction to HR Analytics


    In this chapter you will learn about the problems addressed by HR analytics, as well as will explore a sample HR dataset that will further be analyzed. You will describe and visualize some of the key variables, transform and manipulate the dataset to make it ready for analytics.

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    Introduction and overview
    50 xp
    Finding categorical variables
    100 xp
    Observing categoricals
    100 xp
    Transforming categorical variables
    50 xp
    Encoding categories
    100 xp
    Getting dummies
    100 xp
    Dummy trap
    100 xp
    Descriptive statistics
    50 xp
    Correlations in the employee data
    50 xp
    Percentage of employees who churn
    100 xp
  2. 4

    Choosing the best turnover prediction model

    In this final chapter, you will learn how to use cross-validation to avoid overfitting the training data. You will also learn how to know which features are impactful, and which are negligible. Finally, you will use these newly acquired skills to build a better performing Decision Tree!

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Employee turnover data


loreLore DiricknicksolomonNick Solomon


Intermediate Python
Hrant Davtyan Headshot

Hrant Davtyan

Assistant Professor of Data Science at the American University of Armenia

Hrant is an Assistant Professor of Data Science at the American University of Armenia and founder of METRIC research center. His courses are concentrated on Data collection, analysis, visualization and reporting using Python and R in all 4 domains of business: customers, people, operations and finance. Hrant also holds PhD in Economics. His research is related to applications of Machine Learning in Economics and Finance.
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