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

Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.

Functions and example notebooks for using/comparing different methods of geochemical multivariate outlier detection.

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

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

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

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

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

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

DDoS detection using anomaly detection in high-speed ITP networks. Comparing Autoencoder, Isolation Forest, Local Outlier Factor, and One-Class SVM across real ITP datasets, different aggregation windows, and feature selections using Pearson’s correlation coefficient.

Fraud transactional detection using Isolation Forest and Local Outlier Factor (LOF) models.

this project uses machine learning to detect fraudulent credit card transactions with local outlier factor and isolation forest algorithms. we’ll evaluate performance and visualize data distribution.