In3x,net,watch,14zwhrd6,dildo,18 May 2026

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

# Let's create a dummy dataset data = [' '.join(tokens)] in3x,net,watch,14zwhrd6,dildo,18

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer # Tokenize (simple split) tokens = text

# 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. feature_names) print("TF-IDF Features:"