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.

Introduction and overview

Finding categorical variables

Observing categoricals

Transforming categorical variables

Encoding categories

Getting dummies

Dummy trap

Descriptive statistics

Correlations in the employee data

Percentage of employees who churn

Introduction to HR Analytics

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.

Splitting the data

Separating Target and Features

Spliting employee data

Introduction to Decision Tree classification

Computing Gini index

Splitting the tree

Predicting employee churn using decision trees

Fitting the tree to employee data

Checking the accuracy of prediction

Interpretation of the decision tree

Exporting the tree

Interpretation of results

Predicting employee turnover

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.

Tuning employee turnover classifier

Pruning the tree

Limiting the sample size

Evaluating the model

Interpreting accuracy metrics

Calculating accuracy metrics: precision

Calculating accuracy metrics: recall

Targeting both leavers and stayers

Calculating the ROC/AUC score

Class imbalance

Balancing classes

Comparison of Employee attrition models

Evaluating the 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!

Hyperparameter tuning

Cross-validation using sklearn

Setting up GridSearch parameters

Implementing GridSearch

Important features for predicting attrition

Interpreting importance

Sorting important features

Selecting important features

Develop and test the best model

Final thoughts

Choosing the best turnover prediction model

Employee turnover data

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.

Intermediate Python

Learn how to use python to process employee data and how to develop a predictive model to analyze your own employee turnover in the form of decision trees.

HR Analytics: Predicting Employee Churn in Python

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

Assistant Professor of Data Science American University of Armenia

AUC score

Let's practice!

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Evaluating the turnover prediction model - Targeting both leavers and stayers

Targeting both leavers and stayers

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