SMS Spam Classifier

Overview

This project implements an SMS spam classifier using machine learning algorithms. The classifier uses the NLTK (Natural Language Toolkit) library to process text data and categorize SMS messages as either "spam" or "ham" (non-spam). The model is trained on a labeled dataset of SMS messages, and it uses various text preprocessing techniques such as tokenization, stopword removal, and TF-IDF vectorization for feature extraction.

Features

• Text preprocessing with NLTK
• Tokenization, stopword removal, and stemming
• TF-IDF vectorization for feature extraction
• Model training using machine learning algorithms
• Evaluation of model performance with accuracy and other metrics

Libraries and Dependencies

• nltk - Natural Language Toolkit for text processing
• scikit-learn - For machine learning models and metrics
• pandas - Data manipulation and analysis
• numpy - For numerical operations

git clone https://github.com/sahankrt20/sms-spam-classifier.git