Fraud Detection Case Study
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Updated
Feb 28, 2017 - Python
Fraud Detection Case Study
Built fraud detection classifiers using gaussian naive bayes and decision tress to identify POIs (persons of interests) and applied machine learning techniques such as features selection, precision and recall, and stochastic gradient descent for optimization in Python.
The fraud identification models were build using Python Scikit-learn machine-learning module.
Detect Fraudulent transactions in credit card usage
Identifying some of the biggest players in the Enron fraud scandal of 2001 that led to the titan's fall.
A module of Fraud detection in Credit card applications
E-Commerce Credit Fraud Detection using Machine Learning
Identification of Enron Employees who may have committed fraud based on the public Enron financial and email.
Machine learning models to automatically predict credit card frauds
Cybersecurity Data Mining Competition 2017
Find phishing kits which use your brand/organization's files and image.
Retweet Us, We Will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services (ASONAM 2018)
Traditional Autoencoder on Fraud Detection
CoReRank: Ranking to Detect Users Involved in Blackmarket-based Collusive Retweeting Activities (WSDM 2019)
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