📱 Detect spam SMS in real-time using machine learning with multiple models for effective filtering in cellular networks.

📱 Detect spam SMS messages using a Machine Learning system, classifying texts as Spam or Ham with high accuracy and efficiency.

SMS spam detection pipeline: dual TF-IDF (word+char) → calibrated Linear SVM, nested CV + threshold tuning (F1) + explainability + robustness tests.

It's not just Kaggle!

This is a SMS Spam Detection Project with Streamlit

A machine learning–based SMS Spam Detection system developed using Python to identify and filter spam messages. The project applies text preprocessing, vectorization techniques, and supervised learning algorithms to classify SMS data accurately. It demonstrates practical implementation of NLP concepts.

🛡️SMSGuard – An advanced Machine Learning–powered SMS Spam Detection system using TF-IDF and models like Naive Bayes, Logistic Regression, and SVM. Includes confusion matrix visualization, real-message testing, and custom SMS predictions. Perfect for cybersecurity, telecom filtering, and ML learning.

A machine-learning based SMS Spam Classifier using NLP and Streamlit.

A deep learning project that uses LSTM neural networks to classify SMS messages as spam or ham. This implementation demonstrates text classification with Recurrent Neural Networks, featuring comprehensive data analysis, model training, and evaluation.

This project classifies SMS messages as spam or ham using a feedforward neural network in PyTorch with a bag-of-words representation. It includes train/validation/test splits, performance evaluation (accuracy, sensitivity, specificity, precision), and saving the trained model and vectorizer for reuse in inference.

this tool is not for any revenge purpose. Please use it only for fun! Use wisely!

This repository presents the code and resources for our research on Bengali SMS spam detection. We employed various transformer-based architectures and a custom dataset to achieve high-performance classification, with our findings submitted for peer review.

This is a web application for the detection of SMS messages created using Streamlit.

An interactive SMS Spam Detection application using Streamlit and machine learning. This app allows users to classify messages as spam or ham and view performance metrics for different models.

A simple project to classify SMS messages as spam or not spam using Naive Bayes and TF-IDF vectorization

A Flask-based web app that detects spam emails/SMS using Multinomial Naive Bayes and TF-IDF. Built with NLP, Scikit-learn, and NLTK for high-accuracy classification.

A machine learning project to classify SMS messages as Spam or Ham (Not Spam) using Natural Language Processing (NLP) techniques and Scikit-learn. This binary classification task uses the UCI SMS Spam Collection Dataset and implements various models including Naive Bayes, SVM, and Logistic Regression with performance tuning.

Identifying and distinguishing spam SMS and Email using the multinomial Naïve Bayes model.

The project leverages Naive Bayes Classifiers, a family of algorithms based on Bayes’ Theorem, which presumes independence between predictive features. This theorem is crucial for calculating the likelihood of a message being spam based on various characteristics of the data.