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  3. Intermediate Predictive Analytics in Python

Exercise

The effect of seasonality

Assume you want to predict whether a candidate donor will donate next month. As predictive variable, you want to include the maximum gift of each donor in the previous month. As you learned in the video, the mean amount of the gifts in July and September are similar, but the gifts are fairly higher in December. In this exercise, you will see how this can influence the performance of your model.

The logistic regression model is created and fitted for you in logreg on the data in July.

Instructions 1/3

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  • 1
    • The test data for July is in test_july. Make predictions for the test data in July and calculate the AUC.
  • 2
    • The test data for September is in test_september. Make predictions for the test data in September and calculate the AUC.
  • 3
    • The test data for December is in test_december. Make predictions for the test data in December and calculate the AUC.