team-implant/imPlant
May 2025 – May 2025
1 Month active
Languages Used
C#JSONJupyter NotebookPython
Technical Skills
API DevelopmentAPI TestingASP.NET CoreAzureBackend DevelopmentData Access
Karolina Krysiak
PROFILE
Karolina Krysiak
Karolina Krysiak developed end-to-end data and machine learning capabilities for the team-implant/imPlant repository, focusing on lighting and environmental measurements. She built a Light Intensity Data API using ASP.NET Core and Entity Framework Core, implementing a robust data model, service layer, REST endpoints, and dependency injection. Karolina also delivered machine learning workflows in Python and Jupyter Notebook, enabling light intensity and soil humidity predictions through linear regression, Random Forest, and Gradient Boosting models. Her work included data ingestion, model training, hyperparameter tuning, and visualization, with comprehensive API and Postman tests ensuring reliability. The features demonstrated strong integration of backend and data science skills.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
3
Lines of code
1,438
Activity Months1
Your Network
8 people
Work History
May 2025
17 Commits • 3 Features
May 1, 2025May 2025 monthly summary for team-implant/imPlant focusing on delivering end-to-end data capabilities and ML-driven predictions for lighting and environmental measurements. Highlights include a new Light Intensity Data API with a complete data model, service layer, REST endpoints, DI wiring, and API tests; ML-driven Light Intensity predictions with data ingestion, model training (linear regression and Random Forest), and visualization; and Environmental Measurements prediction using Gradient Boosting and Random Forest with endpoints and JSON outputs. Minor maintenance improvements included a naming consistency fix and test coverage via API/Postman tests.
17 Commits • 3 Features
May 1, 2025May 2025 monthly summary for team-implant/imPlant focusing on delivering end-to-end data capabilities and ML-driven predictions for lighting and environmental measurements. Highlights include a new Light Intensity Data API with a complete data model, service layer, REST endpoints, DI wiring, and API tests; ML-driven Light Intensity predictions with data ingestion, model training (linear regression and Random Forest), and visualization; and Environmental Measurements prediction using Gradient Boosting and Random Forest with endpoints and JSON outputs. Minor maintenance improvements included a naming consistency fix and test coverage via API/Postman tests.
May 2025
Activity
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Quality Metrics
Correctness86.4%
Maintainability87.0%
Architecture87.0%
Performance78.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C#JSONJupyter NotebookPython
Technical Skills
API DevelopmentAPI TestingASP.NET CoreAzureBackend DevelopmentData AccessData AnalysisData ModelingData PreprocessingData ScienceData Transfer ObjectsData VisualizationDatabase DesignDatabase InteractionDependency Injection
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline