Developed a Naive Bayes classifier for classifying the E-mail is Spam or Ham message

C++ File Search Engine for Enron Email Sample Dataset

Machine learning algorithms applied to explore Enron email dataset and figure out patterns about people involved in the scandal.

Generator of priority tokens based on email based content using 2 different methods: TFIDF and a Dictionary

Project work done as part of Udacity's Data Analyst Nanodegree course.

Email Datasets can be found here

Evolutionary Community Detection in Dynamic Social Networks (IJCNN 2019)

Detecting the Evolving Community Structure in Dynamic Social Networks (WWWJ 2020)

This is a Spam/Ham detector using Naive Bayes classifier implemented from scratch in Python3. It is currently trained on Enron dataset.

A priority based email queue solution to address this problem. Also, further extended this feature with an automatic response recommendation that would help in cleaning mails faster.

Distributed Real Time Spam Classification using Apache Spark

C-blondel: an efficient louvain-based dynamic community detection algorithm

An application which converts Enron dataset into a single CSV file

Phishing Detection classifier to filter fraudolent and phishing e-mail.

This repository contains code for normalizing the Enron dataset.

Maximizing Enron's Goldmine with Topic Modeling & Summarization

The Indexer crawls over the enron email dataset folders and indexed each file in the ZincSearch database. It also have a User Interface built with vue which allows you to search over the indexed files based on a keyword.

Enron emails: indexer, apiREST, and visulizer.