SMS Spam Classification Model This repository contains a machine learning model designed to classify SMS messages as either "spam" or "ham" (non-spam). Leveraging a combination of natural language processing (NLP) techniques and supervised learning algorithms, the model aims to provide accurate and efficient spam detection.

Features

Dataset: A curated dataset of labeled SMS messages, including both spam and legitimate messages.

Preprocessing: Techniques such as tokenization, stop-word removal, and stemming/lemmatization are employed to clean and prepare the text data.

Modeling: Various machine learning algorithms are implemented and compared, including Naive Bayes, Support Vector Machines, and many more.

Evaluation: The model's performance is evaluated using metrics like accuracy and precision.

Prediction: Instructions for predicting the model in a production environment are done in an python environment.

Contributing

We welcome contributions from the community. Please see our Contributing Guidelines for more details.