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

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

Anti-Spam bot for Telegram and anti-spam library

Spam Scanner is a Node.js anti-spam, email filtering, and phishing prevention tool and service. Built for @ladjs, @forwardemail, @cabinjs, @breejs, and @lassjs.

Asynchronous Python Wrapper For A.R.Q API.

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

Spam Filtering Techniques for Short Message Service

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

Classic ML starter project to classify messages from a sms dataset into spam and ham

State-of-the-Art Text Classification using Deep Contextual Word Representations

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

Group Guardian is a Telegram bot for admins to maintain a safe community.

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.

Detects cyber threats to the end user with machine learning. This tool can do malware analysis of given exe file, spam analysis of given url and mail.

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

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

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

Companion to Kopano Core kopano-spamd, process spam written to /var/lib/kopano/spamd/spam

Spam Classifier project for my end-of-semester project for Intro to AI class. We were a group of four people. I worked on all the Naive Bayes models.