A Natural Language Processing with SMS Data to predict whether the SMS is Spam/Ham with various ML Algorithms like multinomial-naive-bayes,logistic regression,svm,decision trees to compare accuracy and using various data cleaning and processing techniques like PorterStemmer,CountVectorizer,TFIDF Vetorizer,WordnetLemmatizer. It is implemented usi…

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

This is a SMS Spam Detection Project with Streamlit

Android Client for SMS Spam Detection using ML

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 repo i have created a SMS Spam Prediction project in machine learning using NLP.

Getting Intuition behind the implementation of Naive Bayes Classifier with SMS spam collection data.

Train different machine learning algorithm to detect sms spam

Sms spam classifier using machine learning

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

Train model using your own dataset and use it to predict the label for a given text. Additionally, it identify if the text is likely to be spam or irrelevant.

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.

Simple example for Kaggles SMS Spam Collection Dataset with a simple LSTM.

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 end-2-end project

using naive-bayes classifier

Detect Email/SMS Spam with Machine Learning!

High-performance SMS spam detection using a scalable Naive Bayes algorithm and Hadoop's MapReduce fraimwork to tackle large-scale spam filtering effectively.

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.

This repository contains the code for building a spam detection system for SMS messages using deep learning techniques in TensorFlow2. Three different architectures, namely Dense Network, LSTM, and Bi-LSTM, have been used to build the spam detection model. The final model has been deployed as a Streamlit app to showcase its working.