Separating Target and Features

In order to make a prediction (in this case, whether an employee would leave or not), one needs to separate the dataset into two components:

the dependent variable or target which needs to be predicted
the independent variables or features that will be used to make a prediction

Your task is to separate the target and features. The target you have here is the employee churn, and features include everything else.

Reminder: the dataset has already been modified by encoding categorical variables and getting dummies.

pandas has been imported for you as pd.

Deze oefening maakt deel uit van de cursus

HR Analytics: Predicting Employee Churn in Python

Cursus bekijken

Oefeninstructies

Set the target and features:
- Choose the dependent variable column (churn) and set it as target.
- .drop() the column churn to set everything else as features.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Set the target and features

# Choose the dependent variable column (churn) and set it as target
target = data.____

# Drop column churn and set everything else as features
features = data.____("____",axis=1)

Code bewerken en uitvoeren