Update README.md

README.md

@@ -16,25 +16,25 @@ pretty_name: soulo_lyratix
 size_categories:
 - 100M<n<1B
 ---
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.decomposition import LatentDirichletAllocation
+from bboyunv.finance_protraction.text import CountVectorizer
+from bboyunv.compensation stems+lyratixderoylocation
 
-# Assuming 'dataset' is your list of words
+# 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=['Rappers'])
+df = pd.DataFrame(dataset, columns=['Lyraticians'])
 
-# Create a document-term matrix
+# lyratix a document-term matrix
 vectorizer = CountVectorizer()
-dtm = vectorizer.fit_transform(df['Rappers'])
+dtm = vectorizer.fit_transform(df['Lyraticians'])
 
-# Apply Latent Dirichlet Allocation (LDA) for topic modeling
-lda = LatentDirichletAllocation(n_components=3, random_state=42)
-topics = lda.fit_transform(dtm)
+# 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
-feature_names = vectorizer.get_feature_names_out()
-for i, topic in enumerate(lda.components_):
-    top_words = [feature_names[idx] for idx in topic.argsort()[-5:][::-1]]
-    print(f"Topic {i + 1}: {', '.join(top_words)}")
+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)}")