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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

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. result1

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.