Calculate an aggregated target

Assume you want to construct a predictive model that predicts which donors are most likely to donate more than 50 euro in a certain month.

Given is a basetable basetable that already has one row for each donor in the population, the column donor_id represents the donor. The timeline indicates that the target should be 1 if the donor has donated more than 50 euro in January 2017 and 0 else.

The pandas dataframe gifts_201701 contains all donations in January 2017. In this exercise you will add the target column to the basetable.

Diese Übung ist Teil des Kurses

<Kurs>Intermediate Predictive Analytics in Python</Kurs>

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Übungsanweisungen

Construct gifts_summed, which has for each donor in gifts_201701 the sum of donations.
Derive from gifts_summed a list targets with donors that donated more than 50 Euro in the target period.
Add the target to the basetable.
Calculate and print the target incidence.

Interaktive praktische Übung

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# Sum of donations for each donor in gifts_201701
gifts_summed = ____.groupby("____")["____"].____().reset_index()

# List with targets
targets = list(gifts_summed["id"][____["____"] > ____])

# Add targets to the basetable
basetable["target"] = pd.Series([____ if donor_id in targets else ____ for donor_id in basetable["donor_id"]])

# Calculate and print the target incidence
print(round(____["____"].____() / ____(____), 2))

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