Preprocessing text features

Here, you'll perform a similar preprocessing pipeline step, only this time you'll use the text column from the sample data.

To preprocess the text, you'll turn to CountVectorizer() to generate a bag-of-words representation of the data, as in Chapter 2. Using the default arguments, add a (step, transform) tuple to the steps list in your pipeline.

Make sure you select only the text column for splitting your training and test sets.

As usual, your sample_df is ready and waiting in the workspace.

Import CountVectorizer from sklearn.feature_extraction.text.
Create training and test sets by selecting the correct subset of sample_df: 'text'.
Add the CountVectorizer step (with the name 'vec') to the correct position in the pipeline.
Fit the pipeline to the training data and compute its accuracy.

Exercise

Preprocessing text features

Instructions