1. Learn
    2. /
  2. Courses
    3. /
  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

Balancing classes

It can significantly affect prediction results, as shown by the difference between the recall and accuracy scores. To solve the imbalance, equal weights are usually given to each class. Using the class_weight argument in sklearn's DecisionTreeClassifier, one can make the classes become "balanced".

Let’s correct our model by solving its imbalance problem:

  • first, you’re going to set up a model with balanced classes
  • then, you will fit it to the training data
  • finally, you will check its accuracy on the test set

The variables features_train, target_train, features_test and target_test are already available in your workspace.

Instructions

100 XP
  • Initialize the Decision Tree Classifier, prune your tree by limiting its maximum depth to 5, and balance the class weights.
  • Fit the new model.
  • Print the accuracy score of the prediction (in percentage points) for the test set.