Chameleon-company/MOP-Code
Aug 2025 – Sep 2025
2 Months active
Languages Used
JSONJupyter NotebookPythonSQL
Technical Skills
API IntegrationData AnalysisData CleaningData PreprocessingData VisualizationExploratory Data Analysis
Kartikeya
PROFILE
Kartikeya
Venkata Rama Kartikeya Bulusu developed an end-to-end analytics platform for urban wellbeing indicators in the Chameleon-company/MOP-Code repository, focusing on Melbourne’s open data. Over two months, he designed and implemented a reproducible data pipeline that fetches, cleans, and preprocesses city data, then performs exploratory analysis and visualizations to support urban planning. Using Python, Pandas, and Scikit-learn, he delivered publication-ready notebooks featuring dendrogram and PCA visualizations, while maintaining rigorous repository hygiene through systematic notebook updates, renaming, and documentation scaffolding. His work emphasized reproducibility, data quality, and clear publication workflows, resulting in a robust, maintainable analytics framework for urban research.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
3
Lines of code
28,415
Activity Months2
Your Network
186 people
Work History
September 2025
11 Commits • 1 Features
Sep 1, 2025In September 2025, delivered a consolidated Melbourne urban wellbeing notebook lifecycle and publication readiness workflow within Chameleon-company/MOP-Code. Key work included creating analysis notebooks, implementing data cleaning and preprocessing steps, updating and renaming notebooks for publication, and delivering visualization components (dendrogram and PCA) as part of a publication-ready artifact. Completed notebook cleanups (deleting test notebooks) and progressed toward publication with explicit readiness indicators (Final Publish and Ready to Publish).
11 Commits • 1 Features
Sep 1, 2025In September 2025, delivered a consolidated Melbourne urban wellbeing notebook lifecycle and publication readiness workflow within Chameleon-company/MOP-Code. Key work included creating analysis notebooks, implementing data cleaning and preprocessing steps, updating and renaming notebooks for publication, and delivering visualization components (dendrogram and PCA) as part of a publication-ready artifact. Completed notebook cleanups (deleting test notebooks) and progressed toward publication with explicit readiness indicators (Final Publish and Ready to Publish).
September 2025
August 2025
6 Commits • 2 Features
Aug 1, 2025In August 2025, delivered the Melbourne Urban Wellbeing Analytics Platform within Chameleon-company/MOP-Code, establishing an end-to-end data-driven analytics framework. The pipeline fetches data from the Melbourne Open Data Portal, cleans/preprocesses it, performs exploratory data analysis, compares Greater Melbourne vs Melbourne municipality, and provides visualization and interpretation to support urban planning decisions. Also created an initial documentation scaffold and performed targeted repository maintenance to improve reproducibility and onboarding. Key stability improvements included cleaning and updating notebooks, correcting file naming, and removing obsolete artifacts, ensuring a clear, reproducible analytics workflow.
August 2025
6 Commits • 2 Features
Aug 1, 2025In August 2025, delivered the Melbourne Urban Wellbeing Analytics Platform within Chameleon-company/MOP-Code, establishing an end-to-end data-driven analytics framework. The pipeline fetches data from the Melbourne Open Data Portal, cleans/preprocesses it, performs exploratory data analysis, compares Greater Melbourne vs Melbourne municipality, and provides visualization and interpretation to support urban planning decisions. Also created an initial documentation scaffold and performed targeted repository maintenance to improve reproducibility and onboarding. Key stability improvements included cleaning and updating notebooks, correcting file naming, and removing obsolete artifacts, ensuring a clear, reproducible analytics workflow.
Activity
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Quality Metrics
Correctness96.4%
Maintainability96.4%
Architecture96.4%
Performance95.4%
AI Usage24.6%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPythonSQL
Technical Skills
API IntegrationData AnalysisData CleaningData PreprocessingData VisualizationDendrogramsExploratory Data AnalysisFoliumGeopandasJupyter NotebookJupyter NotebooksMachine LearningMatplotlibNumPyPandas
Repositories Contributed To
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