SMS Spam detection Using Machine Learning

SMS and Email Spam Classifier

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

This Project Predicts whether the Email/SMS is spam or ham by using the extensive knowledge of NLP and various ML Algorithms. Deployed on Streamlit & Herokuapp

SMS Guard is a production-ready Android application that detects SMS fraud in real time, entirely on-device, with no internet connection required. It was built for the ISEA National Hackathon 2026, organized under India's Information Security Education and Awareness (ISEA) initiative, hosted at IIT Ropar.

TRAI‑aware Indian SMS scam detector that fine‑tunes MobileBERT on real + synthetic SMS, exports ONNX for on‑device inference, and ships a Flutter‑ready model pipeline.

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.

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.

It's not just Kaggle!

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.

SMS and Email Spam Classifier end-to-end project, deployed on Streamlit

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.

This is an SMS-Spam-Detection system where you can check any mails/messages whether spam or not.

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

NLP Projects

A Machine Learning Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like MultinomialNB, LogisticRegression, SVC, DecisionTreeClassifier, RandomForestClassifier, KNeighborsClassifier, AdaBoostClassifier, BaggingClassifier, ExtraTreesClassifier, GradientBoostingClassifier, XGBClassifier to compare accuracy.

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.

Classifies SMS messages as spam or non-spam using machine learning

The SMS Spam Detection System is a Django-based application that classifies messages as spam or ham using machine learning and keyword-based filtering. It detects spam by analyzing suspicious words, patterns, and NLP techniques to improve accuracy.