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
Worked on the team-implant/imPlant repository to deliver end-to-end data and machine learning capabilities for lighting and environmental measurements. Developed a Light Intensity Data API using ASP.NET Core and Entity Framework Core, implementing a complete data model, service layer, REST endpoints, and dependency injection. Built machine learning workflows in Python and Jupyter Notebook for light intensity and environmental predictions, utilizing linear regression, Random Forest, and Gradient Boosting, with model training, hyperparameter tuning, and visualization. Enhanced code quality through consistent naming, consolidated dependency injection, and comprehensive API testing with Postman, focusing on maintainability and reliability across backend and data science components.
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 peopleWork 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