Built LDA and QDA models on variables obtained from Principal Component Analysis (PCA) and Kolmogorov-Smirnov (KS) and tuned by leave-one-out cross-validation (LOOCV) to predict fraudulent online advertising click traffic
Script to predict fraud clicks in ads.
Outlier Detection Using Cluster Analysis
The aim of this project is to build a classifier that can detect credit card fraudulent transactions. I used a variety of machine learning algorithms like Decision Trees, Logistic Regression, Artificial Neural Networks and finally, Gradient Boosting Classifier
A program based on semi-supervised learning to detect CerditCard Fraud Detection on Very Unbalanced database
Human or Robot? Predict if an online bid is made by a machine or a human.
The repository contains implementations of different univariate outlier detection algorithms
h2o is a ML library that can be used with R and Python. Here are some R examples for supervised and unsupervised methods.
This is an UPDATED deep statistical data analysis of the 2020 Presidential Race at the federal, state, and local county level. Benford's Law analysis was conducted at each level to detect for ballot fraud and manipulation.
The methods and results of the publication "Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches" are described in more detail in this appendix. The R-syntax for the calculation is provided, as well as a pseudo data set with which the syntax can also be tested.
A Wald statistic for group-level cheating detection
Fraudulent websites detector built with nonparametric models
The Wirecard scandal is considered one of the largest financial scandals of the decade, which caused losses of several billion euros. This analysis examines the digit structure of Wirecard's financial figures in the period from 2005 to 2019 by analyzing the conformity with the expected frequency distributions according to Benford's law. The resu…
Credit card fraud detection using machine learning techniques
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