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Rough
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sums=np.array(len(words))
top_20=sorted(words.items(), key=operator.itemgetter(1), reverse = True)[:20]
print(top_20)
coherence_model_lda=CoherenceModel(model=ldamodel, texts=clean_headlines, dictionary=dictionary, coherence='c_v')
coherence_lda=coherence_model_lda.get_coherence()
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X_train_temp[['city']]=pd.get_dummies(X_train[['city']])
X_train_temp[['county']]=pd.get_dummies(X_train[['county']])
X_train_temp[['state']]=pd.get_dummies(X_train[['state']])
X_val_temp[['city']]=pd.get_dummies(X_train[['city']])
X_val_temp[['county']]=pd.get_dummies(X_train[['county']])
X_val_temp[['state']]=pd.get_dummies(X_train[['state']])
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