课程

HR Analytics: Predicting Employee Churn in Python

中级技能水平

更新时间 2026年4月

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

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PythonMachine Learning4 小时14 视频44 练习3,500 经验值8,866成就声明

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课程描述

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.

先决条件

Intermediate Python

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.

Introduction and overview

50 经验值

Finding categorical variables

100 经验值

Observing categoricals

100 经验值

Transforming categorical variables

50 经验值

Encoding categories

100 经验值

Getting dummies

100 经验值

100 经验值

Descriptive statistics

50 经验值

Correlations in the employee data

50 经验值

Percentage of employees who churn

100 经验值

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.

Splitting the data

50 经验值

Separating Target and Features

100 经验值

Spliting employee data

100 经验值

Introduction to Decision Tree classification

50 经验值

Computing Gini index

100 经验值

Splitting the tree

100 经验值

Predicting employee churn using decision trees

50 经验值

Fitting the tree to employee data

100 经验值

Checking the accuracy of prediction

100 经验值

Interpretation of the decision tree

50 经验值

Exporting the tree

100 经验值

Interpretation of results

50 经验值

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.

Tuning employee turnover classifier

50 经验值

Pruning the tree

100 经验值

Limiting the sample size

100 经验值

Evaluating the model

50 经验值

Interpreting accuracy metrics

50 经验值

Calculating accuracy metrics: precision

100 经验值

Calculating accuracy metrics: recall

100 经验值

Targeting both leavers and stayers

50 经验值

Calculating the ROC/AUC score

100 经验值

Class imbalance

50 经验值

Balancing classes

100 经验值

Comparison of Employee attrition models

100 经验值

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!

Hyperparameter tuning

50 经验值

Cross-validation using sklearn

100 经验值

Setting up GridSearch parameters

100 经验值

Implementing GridSearch

100 经验值

Important features for predicting attrition

50 经验值

Interpreting importance

50 经验值

Sorting important features

100 经验值

Selecting important features

100 经验值

Develop and test the best model

100 经验值

Final thoughts

50 经验值

HR Analytics: Predicting Employee Churn in Python

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