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  3. HR Analytics: Predicting Employee Churn in Python
  • 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.

  • 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.

  • 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.

  • 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!

Initializing

Exercise

Computing Gini index

The decision tree algorithm aims to achieve partitions in the terminal nodes that are as pure as possible. The Gini index is one of the methods used to achieve this. It is calculated based on the proportion of samples in each group.

Given the number of people who stayed and left respectively, calculate the Gini index for that node.

Instructions

100 XP
  • Calculate the total number of employees in that node.
  • Compute the Gini index based on proportion of employees in each group.