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.
Questo esercizio fa parte del corso
HR Analytics: Predicting Employee Churn in Python
Istruzioni dell'esercizio
- Set the target and features:
- Choose the dependent variable column (
churn) and set it astarget. .drop()the columnchurnto set everything else asfeatures.
- Choose the dependent variable column (
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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)