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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

Fitting the tree to employee data

A train/test split provides the opportunity to develop the classifier on the training component and test it on the rest of the dataset. In this exercise, you will start developing an employee turnover prediction model using the decision tree classification algorithm. The algorithm provides a .fit() method, which can be used to fit the features to the model in the training set.

Reminder: both target and features are already split into train and test components (Train: features_train, target_train, Test: features_test, target_test)

Instructions

100 XP
  • Import the classification algorithm called DecisionTreeClassifier.
  • Initialize it as model and set the random state to 42.
  • Apply the decision tree model by fitting the training set features to the model.