Meaning of evolution

In this exercise you will investigate the link between the variable "donations2017min_2016" that you added to the basetable in the previous exercises and the target, using a predictor insight graph.

For your convenience, the methods to create the predictor insight graph are pre-programmed.

To plot the predictor insight graph of a continuous variable variable in a basetable, you can follow these steps:

Discretize the variable in n_bins bins:

basetable["variable_disc"] = pd.qcut(basetable["variable"], n_bins)

Construct the predictor insight graph table:

pig_table = create_pig_table(basetable, "target","variable_disc")

Plot the predictor insight graph based on this table:

plot_pig(pig_table,"variable_disc")

This exercise is part of the course

Intermediate Predictive Analytics in Python

View Course

Exercise instructions

Discretize the evolution variable donations_2017_min_2016 in 5 bins and add it to the basetable.
Create the predictor insight graph table for this variable.
Plot the predictor insight graph of this variable.

Hands-on interactive exercise

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

# Discretize the variable in 5 bins and add to the basetable
basetable["donations_2017_min_2016_disc"] = pd.____(____["____"], ____)

# Construct the predictor insight graph table
pig_table = ____(____, "target", "____")

# Plot the predictor insight graph
plot_pig(____, "____")

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