Tidy anomaly detection

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

⏰ Anomaly Detection with R (separately maintained fork of Twitter's AnomalyDetection 📦)

anomaly detection with anomalize and Google Trends data

Analyzing the safety (311) dataset published by Azure Open Datasets for Chicago, Boston and New York City using SparkR, SParkSQL, Azure Databricks, visualization using ggplot2 and leaflet. Focus is on descriptive analytics, visualization, clustering, time series forecasting and anomaly detection.

Portfolio in R

Extended Isolation Forests for Anomaly/Outlier Detection in R

Anomaly Detection in R - the tidy way using anomalize

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

Repository for Udemy Course: Identify problems with Artificial Intelligence

Scripts to populate my local Postgres database with anomaly datasets

This tool is used to find anomalies or suspicious login events, especially to detect lateral movement.

cbar: Contextual Bayesian Anomaly Detection in R

Predicting disease spread, a DrivenData competition. I'am currently participating in this competition. I used it as submission for the second capstone project in the course 'Professional Certificate in Data Science' provided by Harvard University (HarvardX) on EDX.

Computationally Efficient Learning of Statistical Manifolds

Machine Learning in R

Anomaly Detection with R

Anomaly detection in R

The goal of iCTC is to detect whether peripheral blood cells have CTCs (circulating tumor cell) or not.

A robust deterministic affine-equivariant algorithm for multivariate location and scatter