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
Over three months, contributed to the hpi-sam/ASE-GenAI repository by building analytics and machine learning features focused on data-driven product insights and model evaluation. Developed SQL-based engagement analytics and quality-assurance analyses, leveraging Python and SQL for data querying and visualization. Implemented end-to-end data preprocessing and XGBoost model development in Jupyter Notebooks, including cross-validation and feature-importance analysis. Enhanced project structure and documentation to support maintainability and stakeholder communication. Extended model evaluation workflows to assess generalization on non-student data, emphasizing fairness and repeatability. Work demonstrated depth in data management, code organization, and technical writing, with attention to reproducibility and repository hygiene.
Overall Statistics
Feature vs Bugs
90%Features
Repository Contributions
12Total
Bugs
1
Commits
12
Features
9
Lines of code
6,318
Activity Months3
Your Network
18 peopleShared Repositories
18
2raymooriMember
TonybodoMember
Christian JacobMember
Christian Adriano, PhD candidateMember
David SchlerethMember
sm1llaMember
hendrix15Member
Henok LachmannMember
ibrahimozkanMember
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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