| 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. |