स्टेप 2: एक वेक्टराइज़र बनाना

# Import the TfidfVectorizer and default list of English stop words
from sklearn.feature_extraction.text import ____, ____

# Build the vectorizer
vect = ____(____=____, ____=(1, 2), ____=200, ____=r'\b[^\d\W][^\d\W]+\b').fit(reviews.review)
# Create sparse matrix from the vectorizer
X = vect.transform(reviews.review)

# Create a DataFrame
reviews_transformed = pd.DataFrame(X.____, columns=vect.____)
print('Top 5 rows of the DataFrame: \n', reviews_transformed.head())