Chameleon-company/MOP-Code
Apr 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPythonHTMLJavaScript
Technical Skills
API IntegrationClusteringData AnalysisData CleaningData VisualizationFolium
Nattakan
PROFILE
Nattakan
Nattakan contributed to the Chameleon-company/MOP-Code repository by developing data-driven mapping and analysis features for Melbourne’s transport and mobility planning. Over two months, Nattakan built interactive Jupyter Notebooks and web visualizations using Python, Pandas, and Leaflet.js, integrating clustering algorithms to group landmarks by pedestrian traffic and parking availability. The work included refactoring data pipelines for clarity and maintainability, organizing use cases for better lifecycle management, and delivering visualizations for tram stops, bicycle network safety, and parking accessibility. Nattakan’s engineering focused on enabling policy-relevant insights through robust data analysis, clear documentation, and structured code organization, with no major bugs reported.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
5
Lines of code
261,106
Activity Months2
Your Network
98 people
Work History
May 2025
8 Commits • 4 Features
May 1, 2025May 2025 performance highlights for Chameleon MOP-Code focusing on delivering data visualization features and improving lifecycle management to enable faster publish cycles and policy-relevant insights.
8 Commits • 4 Features
May 1, 2025May 2025 performance highlights for Chameleon MOP-Code focusing on delivering data visualization features and improving lifecycle management to enable faster publish cycles and policy-relevant insights.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Enhanced Melbourne parking accessibility notebook with clustering analysis (KMeans) to group landmarks by pedestrian traffic and parking availability; refactored data loading/processing for reliability and clarity; expanded visualizations of clusters and sensor locations to support planning decisions; maintained traceability through progressive commits.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025: Enhanced Melbourne parking accessibility notebook with clustering analysis (KMeans) to group landmarks by pedestrian traffic and parking availability; refactored data loading/processing for reliability and clarity; expanded visualizations of clusters and sensor locations to support planning decisions; maintained traceability through progressive commits.
Activity
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Quality Metrics
Correctness85.6%
Maintainability85.6%
Architecture83.6%
Performance80.0%
AI Usage27.2%
Skills & Technologies
Programming Languages
HTMLJavaScriptJupyter NotebookPython
Technical Skills
API IntegrationClusteringCode OrganizationData AnalysisData CleaningData VisualizationFile ManagementFoliumGeospatial AnalysisJupyter NotebooksLeaflet.jsMachine LearningMappingMapping LibrariesMatplotlib
Repositories Contributed To
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