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 Exercises6,292 Learners
3500 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

    Free

    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. 2

    Predicting employee turnover

    This chapter introduces one of the most popular classification techniques: the Decision Tree. You will use it to develop an algorithm that predicts employee turnover.

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    Splitting the data
    50 xp
    Separating Target and Features
    100 xp
    Spliting employee data
    100 xp
    Introduction to Decision Tree classification
    50 xp
    Computing Gini index
    100 xp
    Splitting the tree
    100 xp
    Predicting employee churn using decision trees
    50 xp
    Fitting the tree to employee data
    100 xp
    Checking the accuracy of prediction
    100 xp
    Interpretation of the decision tree
    50 xp
    Exporting the tree
    100 xp
    Interpretation of results
    50 xp
  3. 3

    Evaluating the turnover prediction model

    Here, you will learn how to evaluate a model and understand how "good" it is. You will compare different trees to choose the best among them.

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    Tuning employee turnover classifier
    50 xp
    Pruning the tree
    100 xp
    Limiting the sample size
    100 xp
    Evaluating the model
    50 xp
    Interpreting accuracy metrics
    50 xp
    Calculating accuracy metrics: precision
    100 xp
    Calculating accuracy metrics: recall
    100 xp
    Targeting both leavers and stayers
    50 xp
    Calculating the ROC/AUC score
    100 xp
    Class imbalance
    50 xp
    Balancing classes
    100 xp
    Comparison of Employee attrition models
    100 xp
  4. 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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    Hyperparameter tuning
    50 xp
    Cross-validation using sklearn
    100 xp
    Setting up GridSearch parameters
    100 xp
    Implementing GridSearch
    100 xp
    Important features for predicting attrition
    50 xp
    Interpreting importance
    50 xp
    Sorting important features
    100 xp
    Selecting important features
    100 xp
    Develop and test the best model
    100 xp
    Final thoughts
    50 xp

Datasets

Employee turnover data

Collaborators

Lore DirickNick Solomon

Prerequisites

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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