Chameleon-company/MOP-Code
Nov 2024 – Dec 2024
2 Months active
Languages Used
HTMLJupyter NotebookMarkdownPythonJSON
Technical Skills
API IntegrationCode OrganizationCode RefactoringData AnalysisData CleaningData Engineering
Cjay Basa
PROFILE
Cjay Basa
Over two months, Chris Basa contributed to the Chameleon-company/MOP-Code repository by developing and refining data science assets that support urban analytics and business forecasting. He built Jupyter notebooks for analyzing Melbourne cafe activity and urban tree canopy heat reduction, applying Python and Pandas for data loading, cleaning, merging, and visualization. Chris standardized documentation and file naming, reorganizing repository structure to improve onboarding and governance. He archived obsolete notebooks to maintain project focus and updated use-case toolkits with new features and assets. His work emphasized code organization, data quality, and maintainability, enabling faster analytics and more reliable decision support for stakeholders.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
4
Lines of code
36,763
Activity Months2
Your Network
186 people
Work History
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 focused on delivering two key features in Chameleon MOP-Code that enhance the use-case toolkit and repository hygiene, driving faster analytics and cleaner project maintenance. The Urban Tree Canopy Heat Reduction (UC00140) feature expanded the use-case toolkit with updated notebooks, the new Use Case Tool (New_Use_Case_Tool.xlsm), and refreshed Use Case Summary (including UC00140_Urban_Tree_Canopy_Heat_Reduction). The Melbourne Cafe Activity Notebook was archived to reduce clutter and keep active use cases aligned with current priorities. These efforts improve decision support for urban planning, accelerate analysts’ workflows, and simplify ongoing maintenance.
3 Commits • 2 Features
Dec 1, 2024December 2024 focused on delivering two key features in Chameleon MOP-Code that enhance the use-case toolkit and repository hygiene, driving faster analytics and cleaner project maintenance. The Urban Tree Canopy Heat Reduction (UC00140) feature expanded the use-case toolkit with updated notebooks, the new Use Case Tool (New_Use_Case_Tool.xlsm), and refreshed Use Case Summary (including UC00140_Urban_Tree_Canopy_Heat_Reduction). The Melbourne Cafe Activity Notebook was archived to reduce clutter and keep active use cases aligned with current priorities. These efforts improve decision support for urban planning, accelerate analysts’ workflows, and simplify ongoing maintenance.
December 2024
November 2024
14 Commits • 2 Features
Nov 1, 2024Month: 2024-11 | Repository: Chameleon-company/MOP-Code Concise monthly summary: During 2024-11, the team delivered two primary workstreams in Chameleon-company/MOP-Code: a Melbourne cafe/restaurant business activity analysis notebook and a documentation naming standardization initiative. The notebook supports data loading for seating capacity and employment data, initial exploration, cleaning, merging, and visualization improvements, laying groundwork for activity forecasting. The documentation changes standardized naming with UC prefixes, underscores, and IDs, updated the Use_Case_Summary.md, and reorganized the repository structure to improve consistency and onboarding. Major data-quality issues were resolved through cleaning and reconciliation, and the documentation updates reduce downstream misreferences. Business value includes improved data-driven decision support for cafes/restaurants, faster onboarding for new contributors, and stronger governance of data science assets. Demonstrated technologies/skills include Python, Pandas, Jupyter notebooks, data visualization, and Git-based collaboration.
November 2024
14 Commits • 2 Features
Nov 1, 2024Month: 2024-11 | Repository: Chameleon-company/MOP-Code Concise monthly summary: During 2024-11, the team delivered two primary workstreams in Chameleon-company/MOP-Code: a Melbourne cafe/restaurant business activity analysis notebook and a documentation naming standardization initiative. The notebook supports data loading for seating capacity and employment data, initial exploration, cleaning, merging, and visualization improvements, laying groundwork for activity forecasting. The documentation changes standardized naming with UC prefixes, underscores, and IDs, updated the Use_Case_Summary.md, and reorganized the repository structure to improve consistency and onboarding. Major data-quality issues were resolved through cleaning and reconciliation, and the documentation updates reduce downstream misreferences. Business value includes improved data-driven decision support for cafes/restaurants, faster onboarding for new contributors, and stronger governance of data science assets. Demonstrated technologies/skills include Python, Pandas, Jupyter notebooks, data visualization, and Git-based collaboration.
Activity
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Quality Metrics
Correctness93.0%
Maintainability93.0%
Architecture91.8%
Performance90.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
HTMLJSONJupyter NotebookMarkdownPython
Technical Skills
API IntegrationCode OrganizationCode RefactoringData AnalysisData CleaningData EngineeringData MergingData ScienceData VisualizationDocumentationFile ManagementFile System ManagementJupyter NotebookJupyter NotebooksMachine Learning
Repositories Contributed To
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