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 📦)

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.

anomaly detection with anomalize and Google Trends data

Portfolio in R

Extended Isolation Forests for Anomaly/Outlier Detection in R

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

Anomaly Detection in R - the tidy way using anomalize

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

This project focuses on automating the analysis and reporting of bibliometric data, specifically targeting the annual production of academic articles. The primary goal is to understand trends, anomalies, and patterns in bibliometric data through a combination of statistical modeling and exploratory data analysis.

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.