SMS and Email Spam Classifier

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 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 based SMS Spam Classifier using NLP and Streamlit.

SMS Spam detection Using Machine Learning

A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomialNB & GaussianNB to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer. It is implemented using LSTM and Word Embeddings to gain accuracy of 97.70% .

using naive-bayes classifier

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.

It's not just Kaggle!

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.

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.

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

Welcome to the "SMS Spam Detector" project! This machine learning model identifies whether a given SMS is spam or not, providing a valuable tool for spam detection and filtering.

A machine learning-based project to detect SMS spam messages with high accuracy, using the SMS Spam Collection Dataset and techniques like supervised learning, text preprocessing, and model comparison.

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