masa-umich/mcnugget
Oct 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data ProcessingData VisualizationMatplotlibNumPyPandasPython
iausin
PROFILE
Iausin
Over a two-month period, contributed to the masa-umich/mcnugget repository by building an end-to-end RPM data processing and visualization pipeline and expanding the McNugget Putty training dataset. Developed Python-based workflows using Pandas and Matplotlib to ingest CSV files, process voltage and time data, compute revolutions per second, and generate time-series plots for rapid data inspection. Enhanced the dataset by adding extensive numerical data points, supporting larger-scale experiments and improved model training. Maintained rigorous version control with traceable commits, ensuring reproducibility and data integrity. The work enabled faster insights, improved data quality checks, and supported scalable, data-driven model development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
381,152
Activity Months2
Your Network
228 peopleSame Organization
@umich.edu214
Avionics CompMember
Almo-oMember
Alexander SmartMember
Abby DukeMember
Adam DiehlMember
adfarthMember
Alex HansenMember
Ali DowairMember
Aniket JivaniMember
Shared Repositories
14
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Monthly summary for December 2024 (masa-umich/mcnugget): Key feature delivered: expansion of McNugget Putty training data by adding numerous numerical data points to mcnugget/putty_new2k.csv, enabling larger-scale experiments and improved model training. Major bugs fixed: none reported this month. Overall impact and accomplishments: increased data coverage and readiness for model training, with traceable changes committed for reproducibility (commit 448d76c55b0bb9c7c67a248c53c4e4a6df28bb5c). Technologies/skills demonstrated: data engineering, dataset curation, CSV handling, and rigorous version control practices. Business value: accelerates model development and validation, supports scalable data pipelines, and strengthens data-driven decision making.
1 Commits • 1 Features
Dec 1, 2024Monthly summary for December 2024 (masa-umich/mcnugget): Key feature delivered: expansion of McNugget Putty training data by adding numerous numerical data points to mcnugget/putty_new2k.csv, enabling larger-scale experiments and improved model training. Major bugs fixed: none reported this month. Overall impact and accomplishments: increased data coverage and readiness for model training, with traceable changes committed for reproducibility (commit 448d76c55b0bb9c7c67a248c53c4e4a6df28bb5c). Technologies/skills demonstrated: data engineering, dataset curation, CSV handling, and rigorous version control practices. Business value: accelerates model development and validation, supports scalable data pipelines, and strengthens data-driven decision making.
December 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024Month 2024-10: Delivered RPM Data Processing and Visualization feature for masa-umich/mcnugget. Implemented end-to-end RPM data handling from CSV ingestion and processing of voltage/time, to computation of revolutions per second and plotting of voltage over time and RPM over time. All work traced to commit de9d8bf76b25c5abf468aa5f67ac7ffd1093deb4. No major bugs reported this period. Impact: provides a ready-to-use data pipeline and dashboards for RPM monitoring, enabling faster insights and improved data quality checks. Demonstrated skills in Python-based ETL, CSV parsing, time-series analysis, plotting, and strong commit traceability.
October 2024
1 Commits • 1 Features
Oct 1, 2024Month 2024-10: Delivered RPM Data Processing and Visualization feature for masa-umich/mcnugget. Implemented end-to-end RPM data handling from CSV ingestion and processing of voltage/time, to computation of revolutions per second and plotting of voltage over time and RPM over time. All work traced to commit de9d8bf76b25c5abf468aa5f67ac7ffd1093deb4. No major bugs reported this period. Impact: provides a ready-to-use data pipeline and dashboards for RPM monitoring, enabling faster insights and improved data quality checks. Demonstrated skills in Python-based ETL, CSV parsing, time-series analysis, plotting, and strong commit traceability.
Activity
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Quality Metrics
Correctness70.0%
Maintainability70.0%
Architecture60.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data ProcessingData VisualizationMatplotlibNumPyPandasPythonSciPy
Repositories Contributed To
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