Data Science and Analytics Tools - Text Classification with TF-IDF and Naive Bayes

 Notes:

• What problem does it solve?
  Helps businesses automatically categorize text data (e.g., emails, reviews, or support tickets) into predefined categories.

• How can businesses or users benefit from customizing the code?
  Custom categories or more advanced models can be added to fine-tune the text classification.

• How can businesses or users adopt the solution further, if needed?
  Can be integrated with email management systems or customer service platforms for automated routing.

Actual Python Code:

import pandas as pd

from sklearn.feature_extraction.text import TfidfVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.model_selection import train_test_split

from sklearn.metrics import classification_report

# Load text data (e.g., emails or customer support tickets)

data = pd.read_csv('text_data.csv')

# Prepare features (TF-IDF) and target variable (categories)

vectorizer = TfidfVectorizer()

X = vectorizer.fit_transform(data['Text'])

y = data['Category']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train a Naive Bayes model

model = MultinomialNB()

model.fit(X_train, y_train)

# Evaluate the model

y_pred = model.predict(X_test)

print(classification_report(y_test, y_pred))