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")

Este ejercicio forma parte del curso

Intermediate Predictive Analytics in Python

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Instrucciones del ejercicio

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.

Ejercicio interactivo práctico

Prueba este ejercicio completando el código de muestra.

# 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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