This project is a SMS spam detection system that leverages Natural Language Processing (NLP) and Machine Learning to identify and classify text messages as either spam or ham (non-spam). The project utilizes popular libraries such as NLTK, Streamlit, and TF-IDF, and it incorporates a machine learning model trained on a dataset from the UCI Machine Learning Repository.
Overview In today's digital age, unwanted SMS spam is a common nuisance. This project aims to tackle this issue by providing an efficient tool to automatically detect and filter out spam messages.
About
SMS Classification: The core feature of this project is its ability to classify incoming SMS messages as spam or ham.
User-Friendly Interface: The project offers a user-friendly interface developed using Streamlit, making it easy for users to interact with the application.
Text Preprocessing: Text data undergoes preprocessing using NLTK for tasks like tokenization, stop-word removal, and stemming.
TF-IDF Vectorization: The text messages are converted into numerical vectors using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization, which is crucial for training the machine learning model.
Machine Learning Model: Machine learning models (e.g., Naive Bayes, Support Vector Machine, or others) are trained and tested on a dataset from the UCI Machine Learning Repository to classify SMS messages.
