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Yash Gupta

PROFILE

Yash Gupta

Over two months, contributed to Prof-Drake-UMD/INST-760-SUMMER25 by establishing a clean, maintainable project foundation through comprehensive repository scaffolding and cleanup. Developed standardized project skeletons and consistent naming conventions, using Python and CSV for core file operations and data management. Implemented test infrastructure and unit testing scaffolds to support robust development and future CI integration. Removed deprecated assets and obsolete directories, streamlining the repository for easier onboarding and reduced maintenance overhead. Leveraged tools such as Pandas and Matplotlib to support data analysis and visualization workflows, ensuring the codebase remained organized, traceable, and ready for rapid feature delivery.

Overall Statistics

Feature vs Bugs

68%Features

Repository Contributions

83Total
Bugs
7
Commits
83
Features
15
Lines of code
25,543
Activity Months2

Work History

August 2025

23 Commits • 2 Features

Aug 1, 2025

Month 2025-08 summary for Prof-Drake-UMD/INST-760-SUMMER25: Delivered foundational repository bootstrap and scaffolding, established a test infrastructure, and cleaned up obsolete artifacts to improve maintainability and onboarding. This baseline supports faster feature delivery and robust CI readiness.

July 2025

60 Commits • 13 Features

Jul 1, 2025

July 2025 (Month: 2025-07) for Prof-Drake-UMD/INST-760-SUMMER25 focused on establishing a clean, scalable project foundation and reducing maintenance overhead. Delivered standardized scaffolds for multiple projects, initiated core file skeletons across directories, and performed extensive repository hygiene. Implemented a consistent naming convention (e.g., project_1.py), added initial placeholders and test scaffolding, and consolidated project structure. Executed comprehensive cleanup to remove the yash_gupta workspace and deprecated assets, eliminating orphaned directories and references to keep the repo lean and maintainable. These efforts set the stage for faster feature delivery, easier onboarding, and reduced risk of regression due to misaligned artifacts.

Activity

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Quality Metrics

Correctness86.8%
Maintainability86.8%
Architecture85.2%
Performance85.8%
AI Usage25.8%

Skills & Technologies

Programming Languages

CSVPythonText

Technical Skills

Basic File OperationsCode CleanupDashData AnalysisData EngineeringData EntryData IngestionData ManagementData VisualizationFile ManagementMatplotlibPandasPlotlyRepository ManagementSeaborn

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

Prof-Drake-UMD/INST-760-SUMMER25

Jul 2025 Aug 2025
2 Months active

Languages Used

CSVPythonText

Technical Skills

Basic File OperationsCode CleanupData AnalysisData EngineeringData EntryData Management