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

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

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.

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

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

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.…

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.

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

ISOLATION FOREST ALGORITHM FOR PIEZO DATA

DataScience stuff for Python

Temperature anomaly detection

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