Predicting whether a new customer will churn
As you saw in the video, to train a model using sklearn
:
- Import the model of interest - here, a Support Vector Classifier:
from sklearn.svm import SVC
- Instantiate it:
svc = SVC()
- Train it, or "fit it", to the data:
svc.fit(telco['data'], telco['target'])
Here, the first argument consists of the features, while the second argument is the label that we are trying to predict - whether or not the customer will churn. After you've fitted the model, you can use the model's .predict()
method to predict the label of a new customer.
This process is true no matter which model you use, and sklearn
has many! In this exercise, you'll use LogisticRegression
.
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.
# Import LogisticRegression