Probabilities

There are four main ways of expressing the prediction from a logistic regression model – we'll look at each of them over the next four exercises. Firstly, since the response variable is either "yes" or "no", you can make a prediction of the probability of a "yes". Here, you'll calculate and visualize these probabilities.

Two variables are available:

mdl_churn_vs_relationship is the fitted logistic regression model of has_churned versus time_since_first_purchase.
explanatory_data is a DataFrame of explanatory values.

This exercise is part of the course

Introduction to Regression with statsmodels in Python

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Hands-on interactive exercise

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

# Create prediction_data
prediction_data = explanatory_data.assign(
  ____
)

# Print the head
print(____)

Edit and Run Code