Chameleon-company/MOP-Code
Aug 2025 – Sep 2025
2 Months active
Languages Used
JSONJupyter NotebookPythonCSSCSVHTML
Technical Skills
API IntegrationData AnalysisData EngineeringData VisualizationGeoPandasGeospatial Analysis
AreebAshraf96
PROFILE
Areebashraf96
Areeb Bana developed an end-to-end educational equity study for the Chameleon-company/MOP-Code repository, analyzing the spatial relationship between Melbourne schools and public transport stops. Using Python, GeoPandas, and Jupyter Notebook, Areeb built a reproducible data pipeline that included data acquisition, cleaning, geospatial processing, and nearest-neighbor distance calculations. The project incorporated clustering and data visualization to identify accessibility gaps, supporting data-driven policy recommendations. Areeb enhanced the workflow with API-based data loading, robust dataset management, and export capabilities to HTML and JSON. The work demonstrated depth in geospatial analysis and reproducibility, enabling transparent, actionable insights for policy-oriented stakeholders.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
2
Lines of code
2,064,728
Activity Months2
Your Network
98 people
Work History
September 2025
6 Commits • 1 Features
Sep 1, 2025September 2025 performance summary for Chameleon-company/MOP-Code. Delivered an end-to-end Educational Equity Study focused on Melbourne school accessibility to public transport, including data acquisition, cleaning, geospatial processing, distance calculations to transport, accessibility clustering, and visualizations to identify equity gaps. Enhanced notebooks, API-based data loading, and robust dataset management (adding new datasets, removing large local files) with export capabilities (HTML/JSON) for Melbourne-focused reports.
6 Commits • 1 Features
Sep 1, 2025September 2025 performance summary for Chameleon-company/MOP-Code. Delivered an end-to-end Educational Equity Study focused on Melbourne school accessibility to public transport, including data acquisition, cleaning, geospatial processing, distance calculations to transport, accessibility clustering, and visualizations to identify equity gaps. Enhanced notebooks, API-based data loading, and robust dataset management (adding new datasets, removing large local files) with export capabilities (HTML/JSON) for Melbourne-focused reports.
September 2025
August 2025
4 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for Chameleon-company/MOP-Code: Delivered an end-to-end Educational equity study focused on the spatial relationship between Melbourne schools and public transport stops. Implemented and iteratively updated a reproducible notebook-based analysis, including data cleaning, nearest-neighbor calculations, and visualizations to identify accessibility gaps and inform policy decisions. The work strengthens the ability to translate geospatial insights into actionable recommendations and enhances reproducibility of the data pipeline.
August 2025
4 Commits • 1 Features
Aug 1, 2025August 2025 performance summary for Chameleon-company/MOP-Code: Delivered an end-to-end Educational equity study focused on the spatial relationship between Melbourne schools and public transport stops. Implemented and iteratively updated a reproducible notebook-based analysis, including data cleaning, nearest-neighbor calculations, and visualizations to identify accessibility gaps and inform policy decisions. The work strengthens the ability to translate geospatial insights into actionable recommendations and enhances reproducibility of the data pipeline.
Activity
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Quality Metrics
Correctness86.0%
Maintainability86.0%
Architecture86.0%
Performance86.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSCSVHTMLJSONJupyter NotebookPython
Technical Skills
API IntegrationData AnalysisData EngineeringData VisualizationFoliumGeoPandasGeospatial AnalysisJupyter NotebookKMeans ClusteringLinear RegressionMatplotlibPandasPythonPython ProgrammingScikit-learn
Repositories Contributed To
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