webeet-io/layered-populate-data-pool-da
Aug 2025 – Aug 2025
1 Month active
Languages Used
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData MappingData ModelingData Processing
Clara Neagu
PROFILE
Clara Neagu
Developed an end-to-end data population pipeline for the webeet-io/layered-populate-data-pool-da repository, focusing on modeling, transforming, and enriching bank location data for Berlin. Leveraged Python, Pandas, and PostgreSQL to clean, normalize, and geocode OpenStreetMap data, ensuring accurate district assignment and robust schema validation. Addressed a critical OSMnx data fetch issue during initial modeling to guarantee reliable data acquisition. Exported processed datasets in GeoJSON and CSV formats to support downstream geospatial analysis. Authored comprehensive documentation and Jupyter notebooks to facilitate reproducibility and onboarding, demonstrating a thorough approach to data engineering, transformation, and database management within a geospatial context.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
4
Lines of code
16,996
Activity Months1
Your Network
45 peopleWork History
August 2025
12 Commits • 4 Features
Aug 1, 2025August 2025 monthly summary for webeet-io/layered-populate-data-pool-da. Delivered an end-to-end data population pipeline for Banks in Berlin, covering data modeling, transformation, enrichment, export, and loading into PostgreSQL. Fixed a critical OSMnx data fetch bug during initial modeling. Prepared documentation and notebooks to support ongoing data population and knowledge transfer.
12 Commits • 4 Features
Aug 1, 2025August 2025 monthly summary for webeet-io/layered-populate-data-pool-da. Delivered an end-to-end data population pipeline for Banks in Berlin, covering data modeling, transformation, enrichment, export, and loading into PostgreSQL. Fixed a critical OSMnx data fetch bug during initial modeling. Prepared documentation and notebooks to support ongoing data population and knowledge transfer.
August 2025
Activity
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Quality Metrics
Correctness84.2%
Maintainability85.0%
Architecture85.0%
Performance76.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData MappingData ModelingData ProcessingData TransformationDatabase ManagementDocumentationETLGeoJSONGeoPandasGeocodingGeospatial AnalysisGeospatial Data Analysis
Repositories Contributed To
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