arvindkrishna87/STAT390_LegalAid_Fall2025
Sep 2025 – Oct 2025
2 Months active
Languages Used
PythonSQL
Technical Skills
Data AnalysisData CleaningData WranglingDocumentationJupyter NotebookMatplotlib
BingoMuse
PROFILE
Bingomuse
Simiao contributed to the arvindkrishna87/STAT390_LegalAid_Fall2025 repository by developing data analysis workflows and enhancing project documentation over a two-month period. They built two Jupyter Notebooks in Python for processing, cleaning, and analyzing call data, supporting multi-file CSV and Excel ingestion, timezone conversion, and aggregation by Correlation ID to uncover call trends. Simiao also maintained repository hygiene by updating documentation files, adding new PDFs, and managing metadata for onboarding and compliance. Their work demonstrated proficiency in data wrangling with pandas and visualization using matplotlib and seaborn, resulting in reproducible analytics pipelines and a maintainable, well-documented codebase.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
3
Lines of code
2,350
Activity Months2
Your Network
93 people
Work History
October 2025
8 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for arvindkrishna87/STAT390_LegalAid_Fall2025. Focused on delivering feature work and improving project documentation to enhance data-driven decision making and developer productivity. Key features delivered include two Jupyter Notebooks for call data trends (processing, cleaning, and analysis) with multi-file CSV/Excel ingestion, timezone conversion, and Correlation ID-based aggregation. Documentation updates were also completed, including new PDFs and metadata adjustments (macOS DS_Store) to ensure consistency and onboarding reliability. No major bug fixes were reported this month; the emphasis was on robust data workflows and comprehensive documentation. Technologies demonstrated include Python, Jupyter, pandas, timezone handling, CSV/Excel I/O, and documentation tooling to support scalable analytics and maintainable codebase.
8 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for arvindkrishna87/STAT390_LegalAid_Fall2025. Focused on delivering feature work and improving project documentation to enhance data-driven decision making and developer productivity. Key features delivered include two Jupyter Notebooks for call data trends (processing, cleaning, and analysis) with multi-file CSV/Excel ingestion, timezone conversion, and Correlation ID-based aggregation. Documentation updates were also completed, including new PDFs and metadata adjustments (macOS DS_Store) to ensure consistency and onboarding reliability. No major bug fixes were reported this month; the emphasis was on robust data workflows and comprehensive documentation. Technologies demonstrated include Python, Jupyter, pandas, timezone handling, CSV/Excel I/O, and documentation tooling to support scalable analytics and maintainable codebase.
October 2025
September 2025
3 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focus: Documentation updates and repository maintenance for arvindkrishna87/STAT390_LegalAid_Fall2025. This month delivered essential documentation improvements, repository hygiene, and metadata updates to support onboarding, compliance, and future development. Commits were made to push to remote main, ensuring the latest docs are available to stakeholders.
September 2025
3 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Focus: Documentation updates and repository maintenance for arvindkrishna87/STAT390_LegalAid_Fall2025. This month delivered essential documentation improvements, repository hygiene, and metadata updates to support onboarding, compliance, and future development. Commits were made to push to remote main, ensuring the latest docs are available to stakeholders.
Activity
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Quality Metrics
Correctness92.8%
Maintainability92.8%
Architecture92.8%
Performance91.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisData CleaningData WranglingDocumentationJupyter NotebookMatplotlibPandasSeaborn
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