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  3. Intermediate Regression with statsmodels in Python
  • 1

    Parallel Slopes

    Free

    Extend your linear regression skills to parallel slopes regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

  • 2

    Interactions

    Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

  • 3

    Multiple Linear Regression

    See how modeling and linear regression make it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

  • 4

    Multiple Logistic Regression

    Extend your logistic regression skills to multiple explanatory variables. You’ll also learn about logistic distribution, which underpins this form of regression, before implementing your own logistic regression algorithm.

Initializing

Exercise

Logistic regression prediction

As with linear regression, the joy of logistic regression is that you can make predictions. Let's step through the prediction flow one more time!

churn and mdl_churn_vs_both_inter are available; itertools.product is loaded.

Instructions 1/2

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Create a DataFrame explanatory_data of all combinations of these explanatory variables:

  • Set time_since_first_purchase to a sequence from minus two to four in steps of 0.1.
  • Set time_since_last_purchase to a sequence from minus one to six in steps of 0.1.

Name the columns of explanatory_data as "time_since_first_purchase" and "time_since_last_purchase".