Message Importance Classifier

Overview

This project implements a Message Importance Classifier using TF-IDF vectorization and the Multinomial Naive Bayes classifier. The goal is to classify messages into important or unimportant categories based on their content.

Features

• 📊 TF-IDF Vectorizer: Utilizes TF-IDF (Term Frequency-Inverse Document Frequency) vectorization to represent text data.
• 🧠 Multinomial Naive Bayes: Implements the Multinomial Naive Bayes classifier for message classification.
• 🏷️ Importance Labeling: Assigns messages into two categories: important or unimportant.

Technologies Used

• 🐍 Python
• 📊 Scikit-learn library

Getting Started

1. Clone the repository to your local machine.
2. Install the required dependencies using pip install -r requirements.txt.
3. Run the message importance classification script using the provided notebook or script.

Dataset

The model was trained and evaluated on Message, which consists of labeled examples for message importance classification.

How to Use

1. Clone the repository.
2. Install dependencies with pip install -r requirements.txt.
3. Run the message importance classification script on your dataset.

Acknowledgments

• 🙌 Built with the Scikit-learn library for machine learning.

Contribute

Feel free to contribute, open issues, or provide suggestions for enhancements.

Future Improvements

• 🔄 Explore different text vectorization techniques.
• 🧪 Experiment with other classifiers for comparison.
• ⚙️ Fine-tune hyperparameters for improved performance.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Contact

For any inquiries or collaborations, please contact Pramodh R at officialpramodh@gmail.com

Enjoy classifying message importance!