from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

Xtr, Xts, Ytr, Yts = train_test_split(X, Y, random_state=34)

mlp = LogisticRegression()
mlp.fit(Xtr, Ytr)

print(classification_report(Yts, mlp.predict(Xts)))

'''
                 precision    recall  f1-score   support

           fuel       0.75      0.81      0.78        26
        housing       0.75      0.75      0.75        32
   money-supply       0.84      0.88      0.86        75
strategic-metal       0.86      0.90      0.88        48
            tea       0.90      0.82      0.86        44
          wheat       0.95      0.88      0.91        59

       accuracy                           0.85       284
      macro avg       0.84      0.84      0.84       284
   weighted avg       0.86      0.85      0.85       284

 '''