The study begins by identifying key deficiencies in traditional spam filtering systems. Classic rule-based methods and machine learning classifiers such as Naïve Bayes, Support Vector Machines (SVM), ...

Insight: Dataset is imbalanced — mostly non-spam messages. Text Preprocessing: Lowercased all text. Removed punctuation, stopwords, special characters. Applied stemming to reduce words to root form.

This is a simple web app that classifies SMS messages as Spam or Not Spam using Natural Language Processing and a Multinomial Naive Bayes model. It is built using Python and Streamlit.