belgio99/smartfactory
Nov 2024 – Dec 2024
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
API DevelopmentBackend DevelopmentData AnalysisJupyter NotebookKPI CalculationPython
gemma_raga
PROFILE
Gemma_raga
Giovanni Ragadini developed and enhanced the KPI calculation engine for the belgio99/smartfactory repository, focusing on analytics, API standardization, and production deployment. He implemented max and min KPI aggregation, energy cost analytics across all machines, and advanced statistical outputs, using Python and Jupyter Notebook for both backend logic and data analysis. His work included standardizing API parameters, introducing machine typology filtering, and improving error handling and documentation for maintainability. By deploying the engine with a uvicorn-based web server, Giovanni ensured production readiness. The depth of his contributions provided accurate, scalable KPI reporting and improved the reliability of business analytics workflows.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
14Total
Bugs
2
Commits
14
Features
7
Lines of code
927
Activity Months2
Your Network
23 peopleSame Organization
@studenti.unipi.it5
Work History
December 2024
4 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for belgio99/smartfactory. Focused on KPI Calculation Engine Enhancements and Stability, delivering reliability improvements and clearer interpretation of KPI metrics for business decision-making.
4 Commits • 1 Features
Dec 1, 2024December 2024 monthly summary for belgio99/smartfactory. Focused on KPI Calculation Engine Enhancements and Stability, delivering reliability improvements and clearer interpretation of KPI metrics for business decision-making.
December 2024
November 2024
10 Commits • 6 Features
Nov 1, 2024November 2024 highlights major progress across KPI engine, energy cost analytics, API standardization, analytics capabilities, and production readiness. Delivered end-to-end KPI enhancements (max KPI calculations, proper max/min aggregation fixes) and energy cost calculations across all machines with clearer outputs. Standardized KPI calculation API with default parameters and machine typology support, enabling consistent reporting and easier reuse. Added advanced analytics (standard deviation and median) with machine_typology filtering, and enhanced KPI notebook outputs for actionable insights. Achieved production readiness by switching to a uvicorn-based web server to serve KPI reports. Fixed critical issues to ensure accurate KPI aggregations and energy cost totals. These changes improve business value by delivering accurate, scalable KPI reporting, energy cost visibility, and a production-grade analytics platform.
November 2024
10 Commits • 6 Features
Nov 1, 2024November 2024 highlights major progress across KPI engine, energy cost analytics, API standardization, analytics capabilities, and production readiness. Delivered end-to-end KPI enhancements (max KPI calculations, proper max/min aggregation fixes) and energy cost calculations across all machines with clearer outputs. Standardized KPI calculation API with default parameters and machine typology support, enabling consistent reporting and easier reuse. Added advanced analytics (standard deviation and median) with machine_typology filtering, and enhanced KPI notebook outputs for actionable insights. Achieved production readiness by switching to a uvicorn-based web server to serve KPI reports. Fixed critical issues to ensure accurate KPI aggregations and energy cost totals. These changes improve business value by delivering accurate, scalable KPI reporting, energy cost visibility, and a production-grade analytics platform.
Activity
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Quality Metrics
Correctness84.2%
Maintainability85.8%
Architecture74.2%
Performance72.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
API DevelopmentBackend DevelopmentCode RefactoringData AnalysisDocumentationError HandlingJupyter NotebookKPI CalculationPythonPython Scripting
Repositories Contributed To
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