from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
# Vectorizer to convert text into a matrix of token counts vectorizer = CountVectorizer() count_features = vectorizer.fit_transform(data) in3x,net,watch,14zwhrd6,dildo,18
# Your data text = "in3x,net,watch,14zwhrd6,dildo,18" from sklearn
# Tokenize (simple split) tokens = text.split(',') from sklearn.feature_extraction.text import CountVectorizer
# Let's create a dummy dataset data = [' '.join(tokens)]
# TF-IDF transformer tfidf = TfidfTransformer() tfidf_features = tfidf.fit_transform(count_features)
# 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.