This is a SMS Spam Detection Project with Streamlit

One of the primary methods for spam mail detection is email filtering. It involves categorize incoming emails into spam and non-spam. Machine learning algorithms can be trained to filter out spam mails based on their content and metadata.

In this project we are using LSTM to classify texts as spam or ham.

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.

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.

Natural Language Processing

In this project, concepts of Natural Language Processing were used with the help of various Classification algorithms. The output will be classified as Spam or Ham.

Spam Classification using Naive Bayes Classifier

This project uses Recurrent Neural Networks (RNNs) to classify SMS messages as spam or ham (legitimate). My goal is to develop an accurate and efficient spam detection system using deep learning techniques.

Classification for SMS Spam Collection Dataset using BERT

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.

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

SMS Spam detection Using Machine Learning

This is a web service for the classification of SMS messages created using Flask.

In this project I build an end-to-end sms spam classifier and hosted it on Streamlit cloud.

An SMS spam classifier that can classify a message into Spam or Ham

This Project it's based on an Sms Spam Classifier that wil be to detect message is spam or ham.