© Kevin Luzbetak
This model consists of a collection of decision trees (the "forest"), where each tree is trained on a random subset of the data. The final prediction is made by averaging the predictions of all the individual trees, which helps reduce overfitting and improves generalization.
The text data (customer questions) is vectorized using the TF-IDF Vectorizer
(TfidfVectorizer) to convert the textual information into numerical features that the model can
understand.
The model is trained to classify customer support questions into various categories such as "Product Support", "Billing", "Order", etc. Given a new customer query, the model can predict which category it belongs to.
#!/usr/bin/env python
#--------------------------------------------------------------------------------------------------------#
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from prettytable import PrettyTable
#--------------------------------------------------------------------------------------------------------#
# Step 1: Create Data Model
DICTIONARY = {
'How do I reset my password?': 'Product Support',
'What are the payment options?': 'Billing',
'Where can I find my purchase history?': 'Billing',
'How can I contact support?': 'Product Support',
'Can I get a refund?': 'Billing',
'Why was I charged twice?': 'Billing',
'How do I change my subscription?': 'Product Support',
'What is the return policy?': 'Billing',
'How do I track my order?': 'Product Support',
'How do I update my billing information?': 'Billing',
'How do I troubleshoot my device?': 'Product Support',
'Why is my payment not going through?': 'Billing',
'How can I cancel my order?': 'Product Support',
'What should I do if I receive a damaged item?': 'Product Support',
'Where can I see my account balance?': 'Billing',
'How do I apply a discount code?': 'Billing',
'Can I exchange a product I purchased?': 'Product Support',
'How do I upgrade my plan?': 'Product Support',
'Why was my account suspended?': 'Product Support',
'Can I change the shipping address for my order?': 'Product Support',
'Where do I find the user manual?': 'Product Support',
'How do I report a missing item in my order?': 'Product Support',
'How do I enable two-factor authentication?': 'Product Support',
'What is the warranty on my product?': 'Product Support',
'How do I pay my outstanding balance?': 'Billing',
'How can I verify my payment method?': 'Billing',
'Why was my refund declined?': 'Billing',
'Can I pay using cryptocurrency?': 'Billing',
'How do I dispute a charge?': 'Billing',
'Where do I see my payment history?': 'Billing',
'What do I do if I forgot my login credentials?': 'Product Support',
'How do I reset my account PIN?': 'Product Support',
'What happens if my subscription expires?': 'Product Support',
'Where can I view my invoice?': 'Billing',
'How do I access technical support?': 'Product Support',
'What are the supported payment methods?': 'Billing',
'Can I change my delivery date?': 'Product Support',
'How do I redeem a gift card?': 'Billing',
'How do I unsubscribe from email notifications?': 'Billing',
'What do I do if my card is declined?': 'Billing',
'Can I update my order before it ships?': 'Product Support',
'How can I find the nearest store?': 'Billing',
'Why is my bill higher than expected?': 'Billing',
'What should I do if I forgot my password?': 'Product Support',
'How do I cancel my subscription?': 'Product Support',
'How do I track my refund?': 'Billing',
'Why was my payment declined?': 'Billing',
'How do I contact technical support?': 'Product Support',
'How do I change my account details?': 'Product Support'
}
#--------------------------------------------------------------------------------------------------------#
# Ensure the lists have the same length
df = pd.DataFrame(list(DICTIONARY.items()), columns=['question', 'category'])
#--------------------------------------------------------------------------------------------------------#
# Step 2: Train-test split
X_train, X_test, y_train, y_test = train_test_split( df['question'],
df['category'],
test_size=0.3,
random_state=42 )
#--------------------------------------------------------------------------------------------------------#
# Step 3: Vectorize text data
vectorizer = TfidfVectorizer()
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)
#--------------------------------------------------------------------------------------------------------#
# Step 4: Build and train the model
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train_vec, y_train)
#--------------------------------------------------------------------------------------------------------#
# Step 5: Make predictions
y_pred = clf.predict(X_test_vec)
#--------------------------------------------------------------------------------------------------------#
# Step 6: Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy * 100:.2f}%")
#--------------------------------------------------------------------------------------------------------#
# Example: Predict the category of a new customer question
# new_question = ["How do I reset my account PIN?"]
#--------------------------------------------------------------------------------------------------------#
new_question = ["Reset my account PIN"]
new_question_vec = vectorizer.transform(new_question)
predicted_category = clf.predict(new_question_vec)
# Display the prediction in a PrettyTable
table = PrettyTable()
table.field_names = ["Customer Question", "Predicted Category"]
table.add_row([new_question[0], predicted_category[0]])
print(table)
#--------------------------------------------------------------------------------------------------------#
Model Accuracy: 53.33%
+----------------------+--------------------+
| Customer Question | Predicted Category |
+----------------------+--------------------+
| Reset my account PIN | Product Support |
+----------------------+--------------------+
The model’s hyperparameters have been adjusted as follows:
n_estimators=200: The number of decision trees in the forest.max_depth=20: The maximum depth of each decision tree.min_samples_split=5: The minimum number of samples required to split an internal node.min_samples_leaf=2: The minimum number of samples required to be at a leaf node.This approach is useful for text classification, particularly for customer support automation, where you want to categorize incoming queries to route them accordingly.
This is the feature set. In this case, it's the customer questions, which represent the input data that the model will use to make predictions.
This is the target set, or the labels. It's the category (either "Product Support" or "Billing") that corresponds to each question. The model will be trained to predict these categories based on the input questions.
This parameter specifies the proportion of the data that should be used for testing. In this case, 30% of the data will be used as the test set, while the remaining 70% will be used for training.
This parameter ensures reproducibility. It sets the seed for the random number generator, so that every time you run the code, the same split between training and testing data occurs. The value 42 is arbitrary; you can use any number. If you omit this parameter, a different split will happen every time you run the code.
© Kevin Luzbetak