Add a model to the pipeline

You're about to take everything you've learned so far and implement it in a Pipeline that works with the real, DrivenData budget line item data you've been exploring.

Surprise! The structure of the pipeline is exactly the same as earlier in this chapter:

the preprocessing step uses FeatureUnion to join the results of nested pipelines that each rely on FunctionTransformer to select multiple datatypes
the model step stores the model object

You can then call familiar methods like .fit() and .score() on the Pipeline object pl.

Complete the 'numeric_features' transform with the following steps:
- get_numeric_data, with the name 'selector'.
- Imputer(), with the name 'imputer'.
Complete the 'text_features' transform with the following steps:
- get_text_data, with the name 'selector'.
- CountVectorizer(), with the name 'vectorizer'.
Fit the pipeline to the training data.
Hit 'Submit Answer' to compute the accuracy!

Take Hint (-30 XP)

Exercise

Add a model to the pipeline

Instructions