dataforgoodfr/13_reveler_inegalites_cinema
Feb 2025 – Feb 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData ProcessingDatabase DesignDatabase Management
ndretex
PROFILE
Ndretex
Contributed to the dataforgoodfr/13_reveler_inegalites_cinema project by establishing a foundational database schema to support scalable data ingestion and analytics for film festival data. Designed and implemented tables for films, directors, producers, distributors, and festivals, including junction tables and initial seeding with international festival data. Enhanced data quality by introducing enrichment processes, such as new tables for chef positions and Python-based name matching using Levenshtein and Jaro-Winkler algorithms. Improved data ingestion workflows by refactoring parsing logic and strengthening extraction notebooks. Leveraged Python, SQL, and SQLAlchemy to ensure traceable, reproducible changes and enable robust cross-dataset matching for data-driven analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
7,403
Activity Months1
Your Network
12 peopleShared Repositories
12
Audrey DuranMember
FitzfoufouMember
Florian_YunMember
hadrien PoinseauxMember
Julien-CommesMember
LudivineLacourMember
Nicolas RevelMember
Paul GabrielMember
lina kaciMember
Work History
February 2025
4 Commits • 3 Features
Feb 1, 2025In February 2025, delivered a solid data foundation for the dataforgoodfr/13_reveler_inegalites_cinema project, enabling scalable data ingestion, improved data quality, and reliable analytics for festival coverage and film data. Key outcomes include schema initialization, data enrichment, and robust ingestion notebooks, all designed to support cross-dataset matching and data-driven decision making for inequalities in cinema access.
4 Commits • 3 Features
Feb 1, 2025In February 2025, delivered a solid data foundation for the dataforgoodfr/13_reveler_inegalites_cinema project, enabling scalable data ingestion, improved data quality, and reliable analytics for festival coverage and film data. Key outcomes include schema initialization, data enrichment, and robust ingestion notebooks, all designed to support cross-dataset matching and data-driven decision making for inequalities in cinema access.
February 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture80.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
Data AnalysisData CleaningData EngineeringData ProcessingDatabase DesignDatabase ManagementPandasPythonSQLSQLAlchemySchema InitializationString Matching Algorithms
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