SMS Spam Classifier

Abstract: This project focuses on classifying SMS messages as spam or ham (not spam) using machine learning models. The system collects a labelled dataset of SMS messages, performs text preprocessing (tokenization, stop-word removal, lemmatization), converts text into numerical features using TF-IDF or Word Embeddings, and trains classifiers such as Logistic Regression, Naive Bayes, and SVM. The model achieving the highest accuracy is used for deployment through a Streamlit web app.

Keywords: Machine Learning, NLP, Spam Detection, SMS Classification, TF-IDF, Streamlit