Contains the thorough experiments made for a FloydHub article on Anomaly Detection

MLgenerator is a web app which help you to generate machine learning starter code with ease.

Anomaly Detection In An IoT-Acquired Environmental Sensor Data

Repository contains USA Stock Market prediction using Financial Fundamental data which involved EDA, Statistical Analysis and Model Building

Kakapo (KAH-kə-poh) implements a standard set of APIs for outlier detection at scale on Databricks. It provides an integration of the vast PyOD library of outlier detection algorithms with MLFlow for tracking and packaging of models and hyperopt for exploring vast, complex and heterogeneous search spaces.

Anomaly detection algorithms using pyOD

A repository that consists some short data science projects I worked on

Desktop application for testing the performance of various anomaly detection algorithms

An airflow provider for anomaly detection.

Anomaly Detection Pipeline automates data preprocessing for unsupervised scenarios without labels.

Проекты, выполненные в ходе обучения в Яндекс.Практикум по профессии "Специалист по Data Science"

The Machine Learning Toolkit

Time series anomaly detection, time series classification & dynamic time warping, performed on a dataset of Canadian weather measurements.

A UI for demonstration of outlier detection.

The performance of the machine learning algorithm also depends on properly detecting outliers in the dataset. Particularly the regression algorithms are very easily influenced by the outliers. In this case, if the dataset is not correctly cleaned by removing the outlier, then the model performance is unlikely to be as expected. PyOD - Python Too…

Anomaly detection (also known as outlier analysis) is a data mining step that detects data points, events, and/or observations that differ from the expected behavior of a dataset. A typical data might reveal significant situations, such as a technical fault, or prospective possibilities, such as a shift in consumer behavior.

IEEE-CIS Fraud Detection with Unsupervised Learning

This project addresses the challenge of identifying anomalies in sales data, aiming to optimize sales strategies by uncovering deviations from expected profit patterns.