Counting words (I)
Once high level information has been recorded you can begin creating features based on the actual content of each text. One way to do this is to approach it in a similar way to how you worked with categorical variables in the earlier lessons.
- For each unique word in the dataset a column is created.
- For each entry, the number of times this word occurs is counted and the count value is entered into the respective column.
These "count" columns can then be used to train machine learning models.
Questo esercizio fa parte del corso
Feature Engineering for Machine Learning in Python
Istruzioni dell'esercizio
- Import
CountVectorizerfromsklearn.feature_extraction.text. - Instantiate
CountVectorizerand assign it tocv. - Fit the vectorizer to the
text_cleancolumn. - Print the feature names generated by the vectorizer.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Import CountVectorizer
____
# Instantiate CountVectorizer
cv = ____
# Fit the vectorizer
cv.____(speech_df['text_clean'])
# Print feature names
print(cv.____)