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one-class-svm

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Outlier_Detection_Tutorials

Reproducible Python tutorials and benchmarks for outlier detection using statistical methods, machine learning, ensembles, autoencoders, tests, exercises, and CI.

  • Updated Jul 30, 2026
  • Jupyter Notebook
isp-ddos-auto-detector

DDoS detection using anomaly detection in high-speed ITP networks. Comparing Autoencoder, Isolation Forest, Local Outlier Factor, and One-Class SVM across real ITP datasets, different aggregation windows, and feature selections using Pearson’s correlation coefficient.

  • Updated May 31, 2026
  • Python

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.

  • Updated Dec 19, 2021
  • Jupyter Notebook

Empowering Sustainable Energy with Artificial Intelligence. 🍃🌍 An intelligent IoT & Machine Learning platform that transforms raw environmental and mechanical sensor data into real-time diagnostics. By predicting everything from optimal operations to critical emergencies before they happen, we are making renewable energy smarter, safer, and more

  • Updated Jul 17, 2026
  • Python

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