Implementation of feature engineering from Feature engineering strategies for credit card fraud

Code for paper 'Avoid touching your face: A hand-to-face 3d motion dataset (covid-away) and trained models for smartwatches'

A parallel implementation of local outlier factor based on Spark

Detect outliers with 3 methods: LOF, DBSCAN and one-class SVM

Package implements a number local outlier factor algorithms for outlier detection and finding anomalous data

An implementation of a density based outlier detection method - the Local Outlier Factor Technique, to find frauds in credit card transactions. For detecting both local and global outliers.

The project is about outlier detection with different methods same as FastVOA, Kmeans, DBScan or LOF, conducted on KDD dataset.

implement of ML-based anomaly detection models to identify cyberattacks from NetFlow data

Machine learning algorithm to detect fraudulent credit card transactions

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

Detecting Fake User Profiles using k-Means and Local Outlier Factor

Analysis of Classical Machine Learning Algorithms for Anomaly Detection in Time Series Data

Insight Data Science DS.2019C.TO project

Anomaly detection using IF, LOF, OC-SVM, Autoencoder.

Deriving the Local Outlier Factor Score

Anomaly detection using unsupervised method is a challenging one. Isolated Random Forest and Local Outlier Factor are the most promising one. They detect outlier with highest recall possible.

This project aims to detect credit card fraud using Anamoly detection techniques such as Isolation Forest and Local Outlier Factor algorithms.

Geospatial-temporal analysis using Holoviews, along with Pandas to combine various types of data in sensible ways to describe common daily routines for GASTech employees and identify up to twelve unusual events or patterns in the data.