UnSupervised and Semi-Supervise Anomaly Detection / IsolationForest / KernelPCA Detection / ADOA / etc.

A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.

Isolation Forest on Spark

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.

offical implementation of TKDE paper "Deep isolation forest for anomaly detection"

⭐ An anomaly-based intrusion detection system.

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

implement the machine learning algorithms by python for studying

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

C++, rust, julia, python2, and python3 implementations of the Isolation Forest anomaly detection algorithm.

Isolation forest implementation in Go

Security Analytics Engine - Anomaly Detection in Web Traffic

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.

An implementation of Isolation forest

Using Unsupervised methods to identify anomalies in user behaviour through IP Profiling

Web Crawler Detection using Unsupervised Algorithms

Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.

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

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.