Dashboard / Repos / Mc Project

Mc Project

F 41 completed
Web App
unknown / python · small
55
Files
50,736
LOC
0
Frameworks
3
Languages
OverviewFiles & MetricsGit ActivityCall GraphSecurityReports

Pipeline State

completed
Run ID
#371724
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
36.40
Framework unique
—
Isolation
—
Last stage change
2026-04-16 18:15:42
Deduplication group #47544
Member of a group with 2 similar repo(s) — canonical #14176 view group →
Top concepts (1)
Web Backend
Provenance: Repobility (https://repobility.com) — every score reproducible from /scan/

AI Prompt

I want to build a machine learning application for data analysis and visualization using Python. The core functionality should involve simulating and analyzing financial data. Specifically, I need scripts to generate lognormal returns, convert monthly data to annual figures, and calculate CPI annual factors. The project should include a main entry point, a PDF report generation capability, and a user interface input handling mechanism. Please structure it using the provided files like `main.py`, `sim_engine.py`, and `tax_engine.py`.
python machine-learning data-analysis finance visualization
Generated by gemma4:latest

Catalog Information

This project is a machine learning-based application for data analysis and visualization.

Description

The mc_project is a Python-based web application that utilizes libraries such as numpy, pandas, and streamlit to provide data analysis and visualization capabilities. It appears to be designed for users who need to explore and understand complex datasets. The project's functionality includes data manipulation, feature engineering, and interactive visualizations.

الوصف

هذا المشروع هو تطبيق برمجي يستخدم تقنيات التعلم الآلي والتحليل البياني لتقديم قدرات التحليل والتصوير البياني للبيانات. يعتبر هذا التطبيق مناسبًا للمستخدمين الذين يحتاجون إلى استكشاف وتحليل البيانات المعقدة. يتضمن المشروع وظائف تحليل البيانات وتصميم الميزات والتصوير البياني التفاعلي.

Novelty

5/10

Tags

data-analysis data-visualization machine-learning interactive-tools data-manipulation feature-engineering

Technologies

numpy pandas streamlit

Claude Models

claude-opus-4.6

Quality Score

F
41.3/100
Structure
20
Code Quality
53
Documentation
26
Testing
0
Practices
68
Security
100
Dependencies
60

Strengths

  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • Missing README file \u2014 critical for project understanding
  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment
  • 2010 duplicate lines detected \u2014 consider DRY refactoring
  • 16 'god files' with >500 LOC need decomposition

Recommendations

  • Add a comprehensive README.md explaining purpose, setup, usage, and architecture
  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a linter configuration to enforce code style consistency
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

10.3h
Tech Debt (A)
A
OWASP (100%)
FAIL
Quality Gate
A
Risk (12)
All rows above produced by Repobility · https://repobility.com
Unknown
License
86.9%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
98.5%
markdown
1.4%
text
0.0%

Frameworks

None detected

Concepts (1)

Scored by Repobility's multi-pass pipeline · https://repobility.com
CategoryNameDescriptionConfidence
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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