Feature engineering on grouped data

You will now build on the previous exercise, by considering one additional feature: the number of unique protocols used by each source computer. Note that with grouped data, it is always possible to construct features in this manner: you can take the number of unique elements of all categorical columns, and the mean of all numeric columns as your starting point. As before, you have flows preloaded, cross_val_score() for measuring accuracy, AdaBoostClassifier(), pandas as pd and numpy as np.

Apply a lambda function on the group iterator provided, to compute the number of unique protocols used by each source computer. You can use set() to reduce the protocol column to a set of unique values.
Convert the result to a data frame with the right shape by providing an index and naming the column protocol.
Concatenate the new data frame with the old one, which is available as X.
Assess the accuracy of AdaBoostClassifier() on this new dataset using cross_val_score().

Exercise

Feature engineering on grouped data

Instructions