Anomaly Detection

Practical Anomaly Detection using Python and scikit-learn Note: This guide is based on scikit-learn official documentation, academic research on anomaly detection algorithms, and documented best practices from the machine learning community. Code examples are derived from scikit-learn tutorials and tested with scikit-learn 1.3+. Anomaly detection identifies data points, events, or observations that deviate significantly from expected patterns within a dataset. According to scikit-learn documentation, unsupervised anomaly detection is particularly valuable when labeled anomalies are scarce or unavailable—common in cybersecurity intrusion detection, fraud prevention, and system health monitoring. ...

Research Disclaimer This tutorial is based on: PyTorch v2.0+ (official deep learning framework) TensorFlow/Keras v2.15+ (alternative framework examples) scikit-learn v1.3+ (preprocessing and metrics) Academic research on autoencoder-based anomaly detection (Goodfellow et al., 2016; Kingma & Welling, 2013) Production deployment patterns from PyTorch Serve and TensorFlow Serving documentation All implementation patterns follow documented best practices for neural network-based anomaly detection. Code examples are complete, tested implementations suitable for production adaptation. Introduction Looking for classical ML approaches? If you’re new to anomaly detection, start with our guide on classical machine learning techniques using scikit-learn. That post covers Isolation Forest, One-Class SVM, and Local Outlier Factor—excellent choices for tabular data and interpretable results. ...