Chameleon-company/MOP-Code
Aug 2025 – Sep 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData VisualizationMachine LearningPythonData CleaningData Science
Shawn Nguyen
PROFILE
Shawn Nguyen
During a two-month period, Nnamdi Benson developed and maintained data science notebooks for the Chameleon-company/MOP-Code repository, focusing on analyzing the relationship between active transport and air quality. He integrated a non-MOP dataset as a dependency, established reproducible data preparation steps, and implemented workflows for data analysis and visualization using Python, Pandas, and Jupyter Notebook. Nnamdi consolidated and updated use-case notebooks to streamline data sources, reducing maintenance overhead and data wrangling time. He introduced z-score analysis for pedestrian and bicycle counts against PM10 and PM2.5, including pre- and post-COVID comparisons, enabling rapid stakeholder insight into transport-related air quality impacts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
20,387
Activity Months2
Your Network
98 people
Work History
September 2025
2 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09: Delivered consolidated Active Transport and Air Quality Analysis notebooks for UC00175 within Chameleon-company/MOP-Code. Streamlined data workflows by updating the use-case notebook to reflect current data sources and removing redundant datasets. Introduced a new data science analysis notebook to compute and visualize z-scores for pedestrian/bicycle counts against PM10/PM2.5, including a pre- and post-COVID comparison. These changes improve reproducibility, reduce maintenance overhead, and enable rapid stakeholder insight into how active transport activity relates to air quality.
2 Commits • 1 Features
Sep 1, 2025Monthly summary for 2025-09: Delivered consolidated Active Transport and Air Quality Analysis notebooks for UC00175 within Chameleon-company/MOP-Code. Streamlined data workflows by updating the use-case notebook to reflect current data sources and removing redundant datasets. Introduced a new data science analysis notebook to compute and visualize z-scores for pedestrian/bicycle counts against PM10/PM2.5, including a pre- and post-COVID comparison. These changes improve reproducibility, reduce maintenance overhead, and enable rapid stakeholder insight into how active transport activity relates to air quality.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025: Delivered UC00175 Active Transport and Air Quality notebook for Chameleon-company/MOP-Code, integrating a non-MOP dataset as a dependency and establishing robust data analysis, visualization, and data preparation steps. This work enhances reproducibility and enables data-driven evaluation of active transport impacts on air quality.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025: Delivered UC00175 Active Transport and Air Quality notebook for Chameleon-company/MOP-Code, integrating a non-MOP dataset as a dependency and establishing robust data analysis, visualization, and data preparation steps. This work enhances reproducibility and enables data-driven evaluation of active transport impacts on air quality.
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture85.0%
Performance85.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData ScienceData VisualizationJupyter NotebookMachine LearningMatplotlibNumPyPandasPythonSeabornTime Series Analysis
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