def NB_func():
    X_train, X_test, y_train, y_test = train_test_split(df['content'], df['cat_id'], random_state=0)
    count_vect = CountVectorizer()
    X_train_counts = count_vect.fit_transform(X_train)
    tfidf_transformer = TfidfTransformer()
    X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
    clf_NB = MultinomialNB().fit(X_train_tfidf, y_train)
    print(clf_NB)

    # save the model to disk
    filename = '../dataanalysis/models/Naive_Bayes.sav'
    pickle.dump(clf_NB, open(filename, 'wb'))

    print()
    '''Print the prediction of the category from the unknown document'''
    # For now its not accurate due to insufficient sample data
    # print("NAIVE BAYES CLASSIFIER: ", clf_NB.predict(count_vect.transform([""])))
    print()

    print("===============================================")
    print("================= NAIVE BAYES =================")
    print("===============================================")
    most_informative_feature_for_binary_classification(tfidf, clf_NB, n=10)

===============================================
================= NAIVE BAYES =================
===============================================
3 -7.788372938139329 abato
3 -7.788372938139329 abdome abdome erythema
3 -7.788372938139329 abdome erythema redness
3 -7.788372938139329 abdomen distended complaint
3 -7.788372938139329 abdomen ex drain
3 -7.788372938139329 abdomen hbs
3 -7.788372938139329 abdomen hbs kidneys
3 -7.788372938139329 abdomen insitu
3 -7.788372938139329 abdomen insitu pinkish
3 -7.788372938139329 abdomen pelvis

4 -6.221523090721391 bloods operationprocedure
4 -6.4003419545043165 absence peripheral oedema
4 -6.5928517017851505 assessmentreassessment indicated indian
4 -6.677711285491995 afternnon cardiovascular presence
4 -6.698249643881156 bread resting
4 -6.781485612022626 assessmentreassessment indicated braden
4 -6.784706622916945 bardia ordered
4 -6.785386746502815 access lips inspect
4 -6.7946948984778395 alzheimers disease gcs
4 -6.849375428834153 alarmdate cohort