SMS Spam Classifier

This project is a web application that allows users to identify SMS messages and get predictions wheather it's a HAM or SPAM message. The application uses various machine learning models, RNN & BERT to classify texts, achieving an accuracy of around 97%.

Table of Contents

• Dataset
• Technologies
• Website Interface
• Conclusion

Dataset

The project used this dataset to train the models:

Technologies Used

• NLTK
• Sklearn, RNN & BERT for model building
• Streamlit

Website Interface

The website has a simple and intuitive interface that allows users to type texts and classify them. The user can type the SMS he recivies in a textbox in the webpage, the application shows the predicted result on wether the text is spam or not.

Conclusion

This project successfully classified texts with an accuracy of around 97%. The use of machine learning models and NLTK for data cleaning and preparation allowed for effective text classification. The user interface also provided a seamless experience for users to type and classify texts. To launch the project, navigate to the server folder and type streamlit app.py in the command prompt or terminal.