📧 A data engineering exercise
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Updated
Feb 4, 2017 - Python
📧 A data engineering exercise
Fraud Detection by finding the Person of Interest (POI)
The fraud identification models were build using Python Scikit-learn machine-learning module.
Email Network Graph generator (Enron) - Utilizes Fusion Tables
Predict whether an individual is a person of interest based on their enron email.
Enron Email Analysis
[Incomplete] A chrome extension that tells you if a mail you're currently drafting is going to be classified as spam or not.
Machine learning algorithms are used to determine some possible people involved in Enron fraud---Udacity project
Identifying and cleaning the outliers of the Enron Dataset.
Machine learning algorithms applied to explore Enron email dataset and figure out patterns about people involved in the scandal.
Identify Fraud from Enron Email
Spam and No Spam text classification with Convolutional Neuronal Network and Word Embedding
Convolutional Neural Network to classify the emails of the enron data set
Email Datasets can be found here
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 Person Of Interest identifier based on ENRON CORPUS data.
The final project for the University of Malta unit Web Intelligence (ICS2205). The 60% component involved an individual analysis on a twitter dataset using NetworkX. The 40% component involves half of group task where an analysis was performed on the enron email dataset using NetworkX.
A project on Extract-Transform-Load (ETL) operations performed on the emails from the infamous enron corpus database.
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