Chameleon-company/MOP-Code
Dec 2024 – Dec 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData OrganizationData VisualizationFoliumGeospatial Analysis
huda abbasi
PROFILE
Huda Abbasi
During December 2024, S222221214 developed a comprehensive health behavior data analysis and geospatial visualization feature for the Chameleon-company/MOP-Code repository. They designed and implemented an end-to-end analytics pipeline using Python and Jupyter Notebook, focusing on organizing and analyzing multi-year mental and physical health metrics. Leveraging Pandas and NumPy for data cleaning and organization, they incorporated Folium to create interactive geospatial maps that visualize health trends across regions. The project included linear regression analysis with Scikit-learn to identify key trends, supporting data-driven decision-making for health programs. Their work enhanced the repository’s data architecture, enabling scalable dashboards and robust cross-year analytics capabilities.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
2,230
Activity Months1
Your Network
186 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Focused on delivering data analytics and geospatial visualization capabilities for health behaviors in MOP-Code. Implemented end-to-end analytics pipeline for mental and physical health metrics across multiple years, added geospatial visualizations with Folium, and completed linear regression analysis to reveal trends. Final health-data contribution committed. This work enables data-driven health program decisions and supports scalable dashboards and reporting.
1 Commits • 1 Features
Dec 1, 2024Month: 2024-12. Focused on delivering data analytics and geospatial visualization capabilities for health behaviors in MOP-Code. Implemented end-to-end analytics pipeline for mental and physical health metrics across multiple years, added geospatial visualizations with Folium, and completed linear regression analysis to reveal trends. Final health-data contribution committed. This work enables data-driven health program decisions and supports scalable dashboards and reporting.
December 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData OrganizationData VisualizationFoliumGeospatial AnalysisNumPyPandasScikit-learn
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