Fraud Detection Case Study
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Updated
Sep 5, 2017 - HTML
Fraud Detection Case Study
The objective of this project is to explore and learn various Machine Learning Algorithm and see how it solves different Business problems. There are various models like Decision tree, Random Forest, Naive Bayes Classifier, linear regression, Logistic regression etc.
Report on the performance of different machine learning algorithms in identifying persons of interest in the Enron Fraud Case
This solution performs Anomaly Detection with Statistical Modeling on Spark. The detection is based on Z-Score calculated on cpu usage data collected from servers.
🔍 | 📈 | Life / Health Insurance Fraud Detection | 📋 | (Codeshahstra Round 1 Hackathon)
The goal of the competition was to predict fraudulent transactions on a dataset with about 40 million instances, with some characteristics similar to the datasets processed by Feedzai.
Detecting fraud on online customer transactions
Fraud Detection of a 6 million row dataset using AWS and Spark
Test repo for the Smartboard project
A book project accompanying the CopyDetect package. The book provides comprehensive coverage of response similarity analysis using R.
A collection of projects where I worked on building anomaly detection pipelines. This rep covers code for EDA, outlier detection, and stock analysis.
Team project for BA810 (Supervised Machine Learning)
Using R Language to predict whether a user will download an app after clicking a mobile app advertisement. Click on the link below to see more details!
Data preprocessing and classification for the detection of fraudulent transactions
This repository contains the code components of work carried out for analyzing the Medical Provider Fraud Detection dataset with the intent to find most important features to crack down the potentially fraud providers.
The visual graph of fraud detection website
Fraud Detection Research - Data Science Capstone Project at Penn State University, University Park Campus
Unsupervised learning application to fraud detection
This GitHub repository provides a comprehensive set of tools and algorithms for detecting fraud anomalies in various data sources. Fraudulent activities can have severe consequences, impacting businesses and individuals alike. With this repository, we aim to empower researchers with effective techniques to identify and prevent fraudulent behavior.
This research goal is to build binary classifier model which are able to separate fraud transactions from non-fraud transactions.
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