Unsupervised clustering and anomaly detection using K-Means, DBSCAN, and LOF. Includes visual comparison and evaluation metrics.
Built a model to detect fraudulent credit card transactions so that the customers of credit card companies are not charged for items that they did not purchase.
To detect Credit Card Fraud by using SVR, Isolation Forest and Local Outlier Factor.
Synthetic-field simulation with 2D spectra, resonance analysis, and anomaly detection (LOF) for dark-matter inspired patterns.
Deriving the Local Outlier Factor Score
Detects anomalies using the Local Outlier Factor (LOF) algorithm, with clear visualizations of normal data, highlighted outliers, and isolated anomaly points.
Benchmarking Autoencoder, Isolation Forest, LOF, SVM, and Hybrid Models for Network Intrusion Detection on UNSW-NB15 with complete statistical validation and ML pipeline.
A project showcasing the use of machine learning in detecting and classifying electrical faults
Anomaly Detection Projects – Hands-on Python portfolio exploring clustering, model-based, and statistical anomaly detection techniques with full workflow, evaluation, and visualization on real-world datasets.
Laws of Form - Complete Corpus of Definitions - To use in LLMs - ChatGPT 4o - Claude
Free Online Truth Table for Laws of Form Expressions - React App - George Spencer Brown
Recognition of anomalies in the data stream in real time. Identify peaks. Fraud detection.
Local Outlier Factor (LOF), a density-based outlier detection technique to find frauds in credit card transactions.
In this repo, different techniques will be done to analyze Anomaly detection
This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets
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