Anomaly-Detection AmirhosseinHonardoust · COMPLETED
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.
github.com/AmirhosseinHonardoust/Anomaly-Detection · ★ 26 · Forks 3 · Size 1.2 MB
SUMMARY
Technologies 9
Scored 9
Observed 9
Practices 6
Evidence 11
Skips 0
COVERAGE
Analyzed 28 files · 58 commits · 0 API calls
TECHNOLOGIES & DEPTH
TOML LANGUAGE Depth 70
1 files · PRODUCTION
Markdown LANGUAGE Depth 70
4 files · PRODUCTION
YAML LANGUAGE Depth 70
3 files · PRODUCTION
Python LANGUAGE Depth 70
12 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
Poetry BUILD_TOOL Depth 80
1 files · CONFIGURATION
NumPy LIBRARY Depth 73
3 files · PRODUCTION, TEST
pandas LIBRARY Depth 81
9 files · PRODUCTION, TEST
pytest TESTING Depth 37
1 files · TEST
PRACTICES
documentation · observedautomated_tests · observedcontinuous_integration · observedcontainerization · absentlinting · observedformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-09-09
Last commit 2026-09-16
Active months 3
Commits 58