Project from seminar "Data Mining in Production"
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Updated
Feb 26, 2021 - Jupyter Notebook
Project from seminar "Data Mining in Production"
Data exploration, anomaly detection, and data generation for oil deposits dataset.
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 hosts with substantial amount of fraudulent traffic using the impression logs for selected IP addresses
This repository provides some recommender engine models.
Comparison of various anomaly detection algorithms using scikit-learn and visualization through Plotly Dash
Detecting weather anomalies for Dublin Airport
Anomaly detection using IF, LOF, OC-SVM, Autoencoder.
Anomaly Detection in Optical Networks
Insight Data Science DS.2019C.TO project
OCS-WAF: a Web Application Firewall based on anomaly detection using One-Class SVM classifier
One-Class SVMs for Document Classification
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.
Anomaly detection for Sequential dataset
Canned estimators and pre-trained models converted for TensorFlow.
Detect outliers with 3 methods: LOF, DBSCAN and one-class SVM
A curated list of awesome resources dedicated to One Class Classification.
This demo shows how to detect the crack images using one-class SVM using MATLAB.
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