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.

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.

SCOPUS research paper's codes - Time Series Anomaly Detection at Industrial Information Systems

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

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

Anomaly Detection using Machine Learning Techniques

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.…

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

ISOLATION FOREST ALGORITHM FOR PIEZO DATA

Analyzing a dataset to understand mental health factors, this project employs Python tools for preprocessing, exploration, segmentation, trend analysis, and modeling.

Developed a Machine Learning Model for SpectoV for an internship second screening round. Generated a Dataset with temperature, strain , vibration as features and class anamoly.

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

DataScience stuff for Python