hpi-sam/ASE-GenAI
Nov 2024 – Feb 2025
3 Months active
Languages Used
MarkdownSQLJavaJupyter NotebookPDFPython
Technical Skills
Database Query OptimizationDatabase QueryingDocumentationLLM InteractionReflectionSQL
HendrikDroste
PROFILE
Hendrikdroste
Hendrik Droste contributed to the hpi-sam/ASE-GenAI repository by developing analytics and machine learning features that improved data-driven decision-making and model evaluation. He implemented SQL-based engagement analytics, quality-assurance analyses, and compared LLM-generated execution plans with PostgreSQL outputs to assess plausibility. Using Python, Pandas, and XGBoost, Hendrik built end-to-end Jupyter Notebook workflows for data preprocessing, feature engineering, and model training, including targeted evaluation on non-student data subsets. His work emphasized maintainable code organization, comprehensive documentation, and repository hygiene, resulting in a well-structured project that supports reproducible research, stakeholder communication, and scalable experimentation across both SQL and machine learning domains.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
9
Lines of code
6,318
Activity Months3
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Focused on expanding model evaluation for non-student data and establishing a repeatable evaluation workflow within the ASE-GenAI project. Delivered a feature to train a model exclusively on non-students and evaluate its impact on performance as part of Mini Project 3 Task 1, enabling targeted assessment of generalization and fairness.
1 Commits • 1 Features
Feb 1, 2025February 2025: Focused on expanding model evaluation for non-student data and establishing a repeatable evaluation workflow within the ASE-GenAI project. Delivered a feature to train a model exclusively on non-students and evaluate its impact on performance as part of Mini Project 3 Task 1, enabling targeted assessment of generalization and fairness.
February 2025
January 2025
7 Commits • 5 Features
Jan 1, 2025Monthly summary for 2025-01 focusing on delivering a compact, business-value oriented update across data processing, model development, documentation, and repo hygiene. Key outcomes include end-to-end notebook-enabled data preprocessing and ML model development with XGBoost, accompanied by cross-validation and feature-importance visualizations; structured project organization for easier onboarding and navigation; and comprehensive documentation and presentation assets to support stakeholder communication. Additionally, repository cleanliness improvements reduce maintenance burden and pave the way for scalable experimentation.
January 2025
7 Commits • 5 Features
Jan 1, 2025Monthly summary for 2025-01 focusing on delivering a compact, business-value oriented update across data processing, model development, documentation, and repo hygiene. Key outcomes include end-to-end notebook-enabled data preprocessing and ML model development with XGBoost, accompanied by cross-validation and feature-importance visualizations; structured project organization for easier onboarding and navigation; and comprehensive documentation and presentation assets to support stakeholder communication. Additionally, repository cleanliness improvements reduce maintenance burden and pave the way for scalable experimentation.
November 2024
4 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for hpi-sam/ASE-GenAI: Implemented SQL-based engagement analytics and quality-assurance analyses, evaluated LLM-generated vs PostgreSQL execution plans, and improved documentation. Delivered structured insights to drive product decisions and maintainable analytics.
4 Commits • 3 Features
Nov 1, 2024November 2024 monthly summary for hpi-sam/ASE-GenAI: Implemented SQL-based engagement analytics and quality-assurance analyses, evaluated LLM-generated vs PostgreSQL execution plans, and improved documentation. Delivered structured insights to drive product decisions and maintainable analytics.
November 2024
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture83.4%
Performance78.4%
AI Usage35.8%
Skills & Technologies
Programming Languages
JavaJupyter NotebookMarkdownPDFPythonSQL
Technical Skills
Bug FixingCode OrganizationCode RefactoringData AnalysisData ManagementData PreprocessingData VisualizationDatabase Query OptimizationDatabase QueryingDocumentationFile ManagementJupyter NotebooksLLM InteractionLLM Prompt EngineeringMachine Learning
Repositories Contributed To
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