hpi-sam/ASE-GenAI
Jan 2025 – Jan 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData ExplorationData LoadingData PreprocessingFeature EngineeringJupyter Notebook Usage
sm1lla
PROFILE
Sm1lla
Developed a notebook-based data analysis and machine learning pipeline for the hpi-sam/ASE-GenAI repository, focusing on code complexity and worker performance. Leveraging Python, Jupyter Notebook, and libraries such as Pandas and Scikit-learn, the workflow enabled data loading, initial exploration, and calculation of the Type-Token Ratio metric for text explanations. The process included preparing training and testing splits to support future modeling efforts. A subsequent notebook expanded the workflow with data cleaning, preprocessing, feature engineering, and model evaluation. This end-to-end approach established a robust foundation for iterative modeling and data-driven insights, though no major bug fixes were required.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
11,779
Activity Months1
Your Network
18 peopleShared Repositories
18
Work History
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for hpi-sam/ASE-GenAI: Implemented a notebook-based data analysis and ML pipeline to study code complexity and worker performance. Delivered an end-to-end notebook workflow for data loading, initial exploration, and calculation of a Type-Token Ratio (TTR) metric for text explanations, plus preparation of training/testing splits for future modeling. A follow-up notebook expands this workflow with data cleaning, preprocessing, feature engineering, and model evaluation. No major bug fixes were observed this month for this repository; feature work sets the stage for rapid modeling iterations and data-driven decision making.
2 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for hpi-sam/ASE-GenAI: Implemented a notebook-based data analysis and ML pipeline to study code complexity and worker performance. Delivered an end-to-end notebook workflow for data loading, initial exploration, and calculation of a Type-Token Ratio (TTR) metric for text explanations, plus preparation of training/testing splits for future modeling. A follow-up notebook expands this workflow with data cleaning, preprocessing, feature engineering, and model evaluation. No major bug fixes were observed this month for this repository; feature work sets the stage for rapid modeling iterations and data-driven decision making.
January 2025
Activity
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Quality Metrics
Correctness70.0%
Maintainability70.0%
Architecture70.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData ExplorationData LoadingData PreprocessingFeature EngineeringJupyter Notebook UsageMachine LearningNLTKPandasPython Libraries (Pandas, NLTK)Scikit-learn
Repositories Contributed To
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