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from sklearn.feature_extraction.text import CountVectorizer

from sklearn.linear_model import LogisticRegression

from sklearn.preprocessing import StandardScaler

vectorizer = CountVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train_vec.toarray())
X_test_scaled = scaler.transform(X_test_vec.toarray())

model = LogisticRegression()
model.fit(X_train_vec, y_train)

accuracy = model.score(X_test_scaled, y_test)

X_new_data = 'agenda for next sitting see minutes'
X_new_data_vec = vectorizer.transform([X_new_data])
X_new_data_scaled = scaler.transform(X_new_data_vec.toarray())
y_pred = model.predict(X_new_data_scaled)
print(y_pred)