Fraud Detection by finding the Person of Interest (POI)
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Updated
Aug 6, 2017 - Jupyter Notebook
Fraud Detection by finding the Person of Interest (POI)
Email Datasets can be found here
📧 A data engineering exercise
[Incomplete] A chrome extension that tells you if a mail you're currently drafting is going to be classified as spam or not.
A Person Of Interest identifier based on ENRON CORPUS data.
The fraud identification models were build using Python Scikit-learn machine-learning module.
Convolutional Neural Network to classify the emails of the enron data set
The code and data for "Are Large Pre-Trained Language Models Leaking Your Personal Information?" (Findings of EMNLP '22)
Spam and No Spam text classification with Convolutional Neuronal Network and Word Embedding
This repository contains code for normalizing the Enron dataset.
Machine learning algorithms applied to explore Enron email dataset and figure out patterns about people involved in the scandal.
Identifying and cleaning the outliers of the Enron Dataset.
Phishing Detection classifier to filter fraudolent and phishing e-mail.
LT2212 V20 Assignment 3: Same-author-classification via feed-forward neural networks: Transformed email text (Enron) into a machine readable representation and built a classifier that determines whether two texts are authored by the same person or not.
A project on Extract-Transform-Load (ETL) operations performed on the emails from the infamous enron corpus database.
Natural Language Processing (NLP) and programmatic data extraction in large scale fraud investigations.
Machine learning algorithms are used to determine some possible people involved in Enron fraud---Udacity project
Identify Fraud from Enron Email
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