Extract Emails from Gmail account, convert to Excel file and classify using various classification algorithms.

Code created for blog series on unsupervised feature/topic extraction from corporate email content. An implementation for cleaning raw email content, data analysis, unsupervised topic clustering for sentiment/alignment and ultimately several deep-learning models for classification. Details at www.avemacconsulting.com.

Flask web app made using machine learning model. It uses mails from authorized user's Gmail and shows mails with categorical label on web app based on the mail messages using preprocessed machine learning model on training dataset.

Final Year Project for BCT: "Ranking Emails Based On Priority"

Naïve Bayes Algorithm is implemented from scratch in order to classify spam and not spam emails.

A machine learning model that predicts whether an email is spam or not.

Email Classification(Linear Classifiers and Bi-LSTMs) and NER using CRF models

A Python Flask backend using decision tree classifiers, ChatGPT, JWT authentication, and MongoDB storage for an email classification system; containerized with Docker for seamless deployment.

ML Based Email Classifier

SPAM Blocker is a program that pretends to be a mail classifier that detects SPAM and HAM (no spam mail).

An efficient text classification pipeline for email subjects, leveraging NLP techniques and Multinomial Naive Bayes. Easily preprocess data, train the model, and categorize new email subjects. Ideal for NLP enthusiasts and those building practical email categorization systems using Python.

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.

Frontend code for an email classification system with Decision Tree classifier, integrated ChatGPT, JWT authentication, MongoDB storage, and Dockerized modules.

This is a team of agents powered by crewai that are able to receive, classify, search, and reply to emails

📧Email classification using a Machine Learning Models. It categorizes emails as either "ABUSIVE" or "NON ABUSIVE" based on their content, allowing users to quickly assess the nature of the email messages they input.

This repository contains a Jupyter notebook implementing the Multinomial Naive Bayes algorithm from scratch for an email classification task of SPAM or HAM. The notebook also includes a comparison of the results obtained with the scikit-learn implementation of Multinomial Naive Bayes.

A machine learning model that classifies emails as spam or non spam

Simple email classifier using word frequency and Logistic Regression

A Repository Submitted in Partial Fulfillment of the Requirements for the Course Prediction and Machine Learning (COE 005)

A project that evaluates different machine learning and deep learning models for spam emails detection