A Command-Line tool to identify the origin of spam!

Several popular machine learning problems, solved using Scikit-learn.

This repository contains the code that I used to do spam detection

Welcome to my GitHub repository! Here, you will find an exciting collection of my machine learning and AI projects. As a passionate data scientist and AI enthusiast, I am constantly exploring and experimenting with cutting-edge techniques to solve real-world problems.

This project detect the spam mail and this prediction model accuracy_socre is 96% percentage . This project build by @GovindHede

The app is capable of detecting that whether the created email is a spam or a ham. The project was created at 4/1/2024 during my ML learning period.

In this project I perform a simple spam detection task over the SMS Spam Collection Data Set (https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection).

Experimenting different neural network architectures for detecting spam emails

Full list of temporary email providers / Liste actualisée de fournisseurs d'emails temporaires

Fine-Tune BERT model for spam detection.

SpamGuard Pro: A foundational reference for spam detection using scikit-learn and Python. Leverage machine learning with a sophisticated prototype under the GNU General Public License v3.0.

Project of my master's degree in Computer Science ("Study and Research in Anti-Spam Systems") - Weka (CLI) approach.

This Machine Learning (ML) Python program aims to detect spam emails using an autoencoder-based learning approach. It first imports necessary libraries for data handling, evaluation metrics, preprocessing, and neural network modeling.

Cyberus is a tool to check the generic and sentimental legitimacy of the message, and it gives an approximate idea of the risk, based on the dataset, on which it has trained, and some machine learning models for predicting the risk quantitatively.

PHP Email Obfuscator Antispam for WordPress

A feature rich URL shortener

Simple client for the Postmark API spam checker.