Logistic regression with logit()

Logistic regression requires another function from statsmodels.formula.api: logit(). It takes the same arguments as ols(): a formula and data argument. You then use .fit() to fit the model to the data.

Here, you'll model how the length of relationship with a customer affects churn.

churn is available.

This exercise is part of the course

Introduction to Regression with statsmodels in Python

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Exercise instructions

Import the logit() function from statsmodels.formula.api.
Fit a logistic regression of has_churned versus time_since_first_purchase using the churn dataset. Assign to mdl_churn_vs_relationship.
Print the parameters of the fitted model.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import logit
____

# Fit a logistic regression of churn vs. length of relationship using the churn dataset
mdl_churn_vs_relationship = ____

# Print the parameters of the fitted model
print(____)

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