How do I apply ML model after it has been trained?

Question

I apologies for the naive question, I've trained a model (Naive Bayes) in python , it does well (95% accuracy). It takes an input string (ie 'Apple Inc.' or 'John Doe') and discerns whether it's a business name or customer name.

How do I actually implement this on another data set? If I bring in another pandas dataframe, how do I apply what the model has learned from the training data to the new dataframe?

The new dataframe has a completely new population and set of strings that it needs to predict whether its a business or customer name.

Ideally I would like to insert into the new dataframe a column that has the model's prediction.

Any code snippets are appreciated.

Sample code of current model:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(df["CUST_NM_CLEAN"], 
                                                    df["LABEL"],test_size=0.20, 
                                                    random_state=1)

# Instantiate the CountVectorizer method
count_vector = CountVectorizer()

# Fit the training data and then return the matrix
training_data = count_vector.fit_transform(X_train)

# Transform testing data and return the matrix. 
testing_data = count_vector.transform(X_test)

#in this case we try multinomial, there are two other methods
from sklearn.naive_bayes import cNB
naive_bayes = MultinomialNB()
naive_bayes.fit(training_data,y_train)
#MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)

predictions = naive_bayes.predict(testing_data)


from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
print('Accuracy score: {}'.format(accuracy_score(y_test, predictions)))
print('Precision score: {}'.format(precision_score(y_test, predictions, pos_label='Org')))
print('Recall score: {}'.format(recall_score(y_test, predictions, pos_label='Org')))
print('F1 score: {}'.format(f1_score(y_test, predictions, pos_label='Org')))

Answer 1

Figured it out.

# Convert a collection of text documents to a vector of term/token counts. 
cnt_vect_for_new_data = count_vector.transform(df['new_data'])

#RUN Prediction
df['NEW_DATA_PREDICTION'] = naive_bayes.predict(cnt_vect_for_new_data)