A Naive Bayes spam/ham classifier based on Bayes' Theorem. A bunch of emails is first used to train the classifier and then a previously unseen record is fed to predict the output.

AI-Mail-Classifier for Techfest IITB competition

Machine learning for filtering out spam in the ENRON spam dataset

Exploratory Analysis of Enron Dataset and Classification using multiple algorithms

a powerful tool designed to declutter and organize your inbox automatically using Cohere's classification API. Leveraging Node.js and the IMAP protocol, it fetches, parses, and classifies your emails into appropriate folders, facilitating a tidy and well-organized mailbox. Get started now to redefine your email management experience!

A Chrome extension designed to automatically classify your Gmail emails' priority using Gemini. EPIC helps you identify which emails to prioritize and which can wait, saving you time and improving your workflow.

Classification of emails received on a mass distribution group

A comprehensive comparision of kNN, Naive Bayes and Neural Network in Text Classification

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.

A ML classification example to train a Naive Bayes model to be able to predict who wrote a document/email, given the words used in it

Machine learning on spam

A machine learning project that classifies emails and SMS messages as spam or not spam (ham) using Natural Language Processing (NLP) techniques and a Multinomial Naive Bayes classifier

AI-powered n8n workflow that auto-labels Gmail emails using Google Gemini - supports real-time processing and bulk labeling of past emails

AI-powered Email Classifier with Reply Generator | FastAPI + React + scikit-learn | ML-based classification (high_priority/normal/spam) + auto-reply suggestions | Docker + Nginx production-ready | Full-stack ML project

A collection of Python scripts designed to streamline various tasks related to managing emails and PDF attachments. Easily extract clean email text, classify emails as automated or human-generated, process PDFs, and automatically fill PDF forms using saved user profile data.

Intelligent Email Classification & Auto-Response System A modern web application that automatically classifies emails as productive/unproductive, detects intent (meeting, approval, budget, notification), and suggests contextual responses. Built with FastAPI, Alpine.js, and deployed on AWS Lambda for serverless scalability.

Open-source phishing classifier achieving high accuracy with machine learning. Demonstrates NLP, RandomForest algorithms, and model optimization. Includes training data, GUI, and performance metrics.

Script de Google Apps Script para organizar automáticamente Gmail mediante etiquetas basadas en dominios. Incluye procesamiento automático, gestión de subdominios, estadísticas visuales y diseño Material Design.

AI-Powered Smart Email Classifier for Enterprise - an intelligent customer support assistant that automates email triage. It classifies category , detects urgency levels (High/Medium/Low), and generates context-aware auto-replies using NLP & Machine Learning. Built with Python, Streamlit and Hugging face.

This repo has email classifiers based on Naive Bayes classifier, Bernouilli Naive Bayes Classifier and Logistic Regression Classifier.