CountVectorizer for text classification

It's time to begin building your text classifier! The data has been loaded into a DataFrame called df. Explore it in the IPython Shell to investigate what columns you can use. The .head() method is particularly informative.

In this exercise, you'll use pandas alongside scikit-learn to create a sparse text vectorizer you can use to train and test a simple supervised model. To begin, you'll set up a CountVectorizer and investigate some of its features.

Import CountVectorizer from sklearn.feature_extraction.text and train_test_split from sklearn.model_selection.
Create a Series y to use for the labels by assigning the .label attribute of df to y.
Using df["text"] (features) and y (labels), create training and test sets using train_test_split(). Use a test_size of 0.33 and a random_state of 53.
Create a CountVectorizer object called count_vectorizer. Ensure you specify the keyword argument stop_words="english" so that stop words are removed.
Fit and transform the training data X_train using the .fit_transform() method of your CountVectorizer object. Do the same with the test data X_test, except using the .transform() method.
Print the first 10 features of the count_vectorizer using its .get_feature_names() method.

Exercise

CountVectorizer for text classification

Instructions