Popular GitHub Repositories for Spam

List of free GPTs that doesn't require plus subscription

Headless chatbot that detects spam and posts links to it in chatrooms for quick deletion.

?️ Tool to generate adversarial text examples and test machine learning models against them

ML based projects such as Spam Classification, Time Series Analysis, Text Classification using Random Forest, Deep Learning, Bayesian, Xgboost in Python

Using word vectors to classify spam messages

A Deep Graph-based Toolbox for Fraud Detection in TensorFlow 2.X

Code for KDD 2020 paper Robust Spammer Detection by Nash Reinforcement Learning

LLM for Email Spam Detection

Large Collection of Extensions and AspNetCore projects for ML.NET models and integration

Email Spam-detection is an ANN app with TensorFlow. The idea is simple - given an email you’ve never seen before, determine whether or not that email is Spam

A web app that classifies text as a spam or ham. I am using my own ML algorithm in the backend, Code to that can be found under machine_learning_section. For Live Demo: Checkout this link

Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

This is a Text Analysis App which can be used to find a detailed analysis of a particular text. This includes 5 main types of Analysis - Spam/Ham Detection, Sentiment Analysis, Stress Detection, Hate & Offensive Content Detection, Sarcasm Detection

Spam filtering module with Machine Learning using SVM (Support Vector Machines).

Quick Hands-On NLTK tutorial for NLP in Python. NLTK is one of the most popular Python packages for Natural Language Processing (NLP). Easy to Start for Anyone.

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 using LSTM and Word Embeddings to gain accuracy of 97.84%.

Natural Language Processing with Python, Python ile Dogal Dil Isleme

End-to-end implementation of Spam Detection in Email using Machine Learning, Python, Flask, Gunicorn, Scikit-Learn, and Logistic Regression on the Heroku cloud application platform.

An Android Project to demonstrate the use of a TensorFlow Lite model to classify spam messages.

A machine learning model created with random forest classifier to show the probability and predict if an email is spam or non spam