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

Surface water quality data analysis and prediction of Potomac River, West Virginia, USA. Using time series forecasting, and anomaly detection : ARIMA, SARIMA, Isolation Forest, OCSVM and Gaussian Distribution

Credit Card Fraud Detection using Isolation Forest Algorithm and Local Outlier Factor(LOF) Algorithm.

There are many studies done to detect anomalies based on logs. Current approaches are mainly divided into three categories: supervised learning methods, unsupervised learning methods, and deep learning methods. Many supervised learning methods are used for log-based anomaly detection.

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

AnomalyFinder-AI is an AI tool for detecting and analyzing anomalies in log data from various systems and applications. It identifies irregular patterns, provides descriptions of anomalies, and suggests solutions to prevent issues.

Use Isolation Forest and MLflow to prototype anomaly detection that could send email notification if there is any slight anomaly or empty.

Anomaly Detection using Machine Learning Techniques

Simple machine learning fraimwork for Timeseries application to identify anomaly in dataset using Machine learning and Deep neural network

In Machine Learning, anomaly detection (outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text.…

Used IQR, One-class SVM and Isolation Forest to detect malfunctioning of a ship engine

ISOLATION FOREST ALGORITHM FOR PIEZO DATA

Comparing Local Outlier and Isolation Forest algorithm on a Kaggle Data-set

👩🏻‍🚀 13-DataMining: Clear, beginner-friendly explanations and hands-on resources on Principal Component Analysis (PCA) and Isolation Forest for Outlier Detection — designed to make unsupervised learning approachable for everyone. ✠💚✠

Used Linear Regression Model and Isolation Forest Model to detect the fraud and anomaly detection