Classifying emails of the Enron dataset into spam or ham with logistic regression and count vectorization.

Classification models for the Enron SPAM / HAM dataset

Spam Classification on the Enron and SMS datasets

Bert Spam Classification – Enron Dataset (Code) by Fabian Jirges

Email classification using Enron dataset into Spam/Ham category

Bert Spam Classification – Enron Dataset (Code) by Fabian Jirges

A supervised classification pipeline to classify emails as spam or non-spam on the Enron email dataset.

A supervised classification pipeline to classify emails as spam or non-spam, dataset - enron pre-processed emails

What it shows: Text preprocessing, ML classification, evaluation metrics. Tech: Python, Scikit-learn, NLTK. Dataset: Enron Spam Dataset. Repo Structure:

A classification project using logistic regression, CART, and Random Forest to identify spam emails from the Enron dataset.

This repository contains the code and analysis for the Enron email classification project. The project aims to develop a supervised classification pipeline to accurately classify emails as spam or non-spam using the Enron email dataset.

We are using Euron-Spam dataset for spam email classification problem. The euron datasets are present at this location: http://nlp.cs.aueb.gr/software_and_datasets/Enron-Spam/index.html .

We will be using Euron spam dataset for spam email classification problem. The euron datasets ar present at the below location: http://nlp.cs.aueb.gr/software_and_datasets/Enron-Spam/index.html

This project implements a text classification pipeline to identify spam using a refined version of the 2007 TREC and Enron datasets.

Production-ready spam/ham email classification using Enron dataset, classical ML (Logistic Regression, SVM), and FastAPI serving. Features modular pipeline, EDA notebooks, and deployable web API.

This repository contains a comprehensive implementation of a spam classification system using an LSTM (Long Short-Term Memory) model in TensorFlow. The project is designed to detect and classify spam emails based on the Enron email dataset. I

The code represents a text classification project focused on email spam detection. It utilizes the Enron email dataset, a widely used benchmark for spam detection. The code preprocesses the email data, splitting it into training and testing sets.

An end-to-end Machine Learning web application that classifies emails as Spam or Ham. Built with Python, Scikit-Learn, and Streamlit, and trained on the real-world Enron dataset to achieve high-accuracy text classification using Natural Language Processing (NLP).

A Machine Learning-based Spam Detection System integrated with a browser extension. Trained on the Enron dataset using TF-IDF and SVM for accurate classification, it predicts spam emails in real time via a Flask API connection. The project ensures high accuracy, strong privacy, and a seamless, user-friendly experience for email users.

Built a spam email classification pipeline from scratch in Python using Multinomial/Bernoulli Naive Bayes and L2-regularized Logistic Regression with Batch, Mini-batch, and SGD. Engineered BoW/Bernoulli features, tuned hyperparameters, and evaluated on Enron datasets using accuracy, precision, recall, and F1-score.

This project implemented a Deep Learning-based Email Spam Detection pipeline using a Bidirectional LSTM architecture trained on combined SpamAssassin and Enron datasets. Using sequence tokenization and a trainable embedding, the Bi-LSTM model achieved strong classification performance (insert your exact metrics from the run).

This thesis develops an email detection and classification system for spam and ham using NLP and Machine Learning. Trained on real datasets (e.g., Enron), it applies algorithms like Naive Bayes, SVM, or Deep Learning (LSTM, BERT). A Thunderbird add-on enables real-time filtering, enhancing user protection.