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.

Utilizing LSTM Neural Networks to forecast energy cosumption trends with time series analysis. Employing Collaborative Filtering with Matrix Factorization and SVD, the system suggests personalized actions based on user behavior, fostering energy conservation.Leveraging Isolation Forest to detect anomalies in consumption patterns.

This project focuses on building a anomaly detection model to detect wafer runs that are anomalous. The dataset does not contain labelled data (anomalous/non-anomalous), therefore an unsupervised learning method is utilised. Python and the Sci-kitLearn machine learning libraries are the primary tools used in this project.

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

The workflow includes data exploration, dimension reduction, and visualization, with the integration of machine learning concepts for advanced analysis. The GitHub repository provides comprehensive documentation and instructions for replicating the analysis and findings.

Analyze motion sensor data to find patterns in a person's behavior

The project explores a range of methods, including both statistical analysis, traditional machine learning and deep learning approaches to anomaly detection a critical aspect of data science and machine learning, with a specific application to the detection of credit card fraud detection and prevention.

Fraud Detection model based on anonymized credit card transactions based on Isolation Forest Algorithm and Local Outlier Factor

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

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

DataScience stuff for Python

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.

Built a model to detect fraudulent credit card transactions so that the customers of credit card companies are not charged for items that they did not purchase.

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

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