This is a simple email and SMS spam classifier built using Naive Bayes. The model achieves an accuracy of 98.2% and performs well on unseen data. The project also includes a user-friendly interface using Streamlit.
To install and run the project, follow these steps:
1. Clone the repository to your local machine.
2. Install the required packages using pip: pip install -r requirements.txt
3. Run the main script: python app.py
Note: The trained model and vectorizer object are included in the repository. If you want to train the model on your own dataset, you can use the spam_project.ipynb notebook provided.
To use the email and SMS spam classifier, follow these steps:
1. Run the main script.
2. Enter the text to be classified as either spam or not.
3. The system will use the trained model to classify the text and display the result.
We welcome contributions to this project. If you want to contribute, please follow these guidelines:
1. Fork the repository.
2. Create a new branch for your changes.
3. Make your changes and commit them with clear commit messages.
4. Push your changes to your forked repository.
5. Create a pull request with a clear description of your changes.
If you have any questions or issues, please contact me at rishabhvyas472@gmail.com