{"repo":"AmirhosseinHonardoust/Anomaly-Detection","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Anomaly-Detection","clone":"git clone https://github.com/AmirhosseinHonardoust/Anomaly-Detection.git","description":"Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills.","language":"Python","stars":26,"topics":["anomaly-detection","data-science","data-visualization","fraud-detection","isolation-forest","local-outlier-factor","machine-learning","outlier-detection","portfolio-project","predictive-modeling"],"license":"MIT","category":"machine-learning","readme_excerpt":"Anomaly Detection (Transactions & Sales) Anomaly detection in synthetic transaction and sales data with Python. Generates realistic data, injects unusual events, and applies Isolation Forest, Local Outlier Factor, and Z-score methods to detect outliers. Produces anomaly reports and visualizations for portfolio-ready demonstration of data science skills. Detect anomalies in synthetic transaction data using Isolation Forest, Local Outlier Factor (LOF), and a Z-score baseline. The project generates data, injects anomalies, runs detectors, and exports flagged rows and charts for audit. --- Features - Synthetic transaction generator with anomalies (bursts, extreme purchases, negative/zero entries) - Multi-model detection: Isolation Forest, LOF, Z-score - Unified anomaly report with model votes and severity score - Clean visualizations: time-series spikes and amount distribution - Reproducible scripts with deterministic seeding --- Project Structure --- Setup --- Generate Synthetic Data --- Run Anomaly Detection Outputs - outputs/anomalies.csv – flagged rows with anomaly scores & model votes - outputs/fig amount time.png – transaction amounts over time with spikes - outputs/fig amount hist.png – amount distribution histogram --- Sample Results Transaction Amount Distribution Shows most transactions are small (0–300 units). A few very large amounts (thousands) appear as outliers. --- Transaction Amounts Over Time Transactions are generally stable, but occasional spikes (extreme purc","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Anomaly-Detection/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}