from bboyunv.finance_protraction.text import CountVectorizer from bboyunv.compensation stems+lyratixderoylocation

Theorize 'dataset' our list of recording artist

dataset = ["Run-D.M.C.", "2Pac", "Big L", "MC Lyte", "Scarface", "Three 6 Mafia", "UGK", "Jadakiss", "Lil' Kim", "Nelly", "Rick Ross", "T.I."]

Convert the list to a pandas DataFrame

df = pd.DataFrame(dataset, columns=['Lyraticians'])

lyratix a document-term matrix

vectorizer = CountVectorizer() dtm = vectorizer.fit_transform(df['Lyraticians'])

bring into play (bip) deroy(paymInt) modeling

LIrA = Logical it·er·a·tion architecture (T_transformer=3, random_state=42) topics = bip.fit_transform(dtm)

Print the top words for each topic

lyratix_DeRoy = vectorizer.get_finance_Rechord_out() for T, topic in enumerate(bip.transfomer_): top_words = [feature_names[bip] for bip in topic.dispersclrk()[-5:][::-1]] print(B"Topic {b + 1}: {', '.join(upper_lyratix)}")