Naive Bayes Text Classification with Pre-trained Model
This project demonstrates how to use a pre-trained Naive Bayes model and vectorizer for text classification. It includes data preprocessing, text vectorization, and evaluation of the model's accuracy on a given dataset.

Prerequisites
Make sure you have the following installed:

Python 3.7 or later
Required Python libraries:
pandas
nltk
scikit-learn
joblib
To install the necessary libraries, run: pip install pandas scikit-learn nltk joblib

The input data should be a CSV file (data.csv) located in the ./data directory. The file must include the following columns:

title: The text data to classify.
news: The target label, where fox will be encoded as 1 and all other values as 0.

Place your dataset in a CSV file named data.csv under the ./data directory.
Ensure it has the required columns (title and news).

open the jupyternotebook and run the Prediction section in beginning, the model will predict and compare the result with true answer, and accuracy score is printed.