AmirhosseinHonardoust/Anomaly-Detection
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.
This project helps data scientists practice and demonstrate their skills in finding unusual patterns within financial transaction or sales records. It takes in simulated transactional data, identifies suspicious activities or outliers like extreme purchases or sudden activity bursts, and outputs detailed anomaly reports and clear visualizations. The primary user is a data science professional looking to build or showcase a portfolio of anomaly detection projects.
No commits in the last 6 months.
Use this if you are a data scientist who needs a reproducible way to generate realistic transaction data with injected anomalies and apply multiple detection techniques to identify and visualize unusual events.
Not ideal if you are looking for a plug-and-play solution for live production systems or real-time anomaly detection on actual streaming data.
Stars
27
Forks
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Language
Python
License
MIT
Category
Last pushed
Sep 11, 2025
Commits (30d)
0
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