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

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.

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.

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.

📧 ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.

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

Finetune Flan-T5 for email classification

A spam email classification model using Naive Bayes with machine learning, designed to categorize emails as spam or not spam.

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

📧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.

Prioritize your mailbox. Intelligently auto-tag, highlight, and organize important emails in Thunderbird using trainable tags.

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

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.

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

ML Based Email Classifier

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

ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.

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.