Dropping unnecessary features
Some features such as 'Area_Code'
and 'Phone'
are not useful when it comes to predicting customer churn, and they need to be dropped prior to modeling. The easiest way to do so in Python is using the .drop()
method of pandas
DataFrames, just as you saw in the video, where 'Soc_Sec'
and 'Tax_ID'
were dropped:
telco.drop(['Soc_Sec', 'Tax_ID'], axis=1)
Here, axis=1
indicates that you want to drop 'Soc_Sec'
and 'Tax_ID'
from the columns.
This exercise is part of the course
Marketing Analytics: Predicting Customer Churn in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Drop the unnecessary features
telco = ____