In3x,net,watch,14zwhrd6,dildo,18 -

# Tokenize (simple split) tokens = text.split(',')

# Viewing features feature_names = vectorizer.get_feature_names_out() print("Features:", feature_names) print("TF-IDF Features:", tfidf_features.toarray()) This example uses CountVectorizer and TfidfTransformer from scikit-learn to create basic features from your text. Adjustments would be needed based on your specific use case and data. in3x,net,watch,14zwhrd6,dildo,18

# Vectorizer to convert text into a matrix of token counts vectorizer = CountVectorizer() count_features = vectorizer.fit_transform(data) # Tokenize (simple split) tokens = text