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
Smilla Fox developed a notebook-based data analysis and machine learning pipeline for the hpi-sam/ASE-GenAI repository, focusing on code complexity and worker performance. Using Python and Jupyter Notebook, Smilla designed an end-to-end workflow that included data loading, initial exploration, and calculation of the Type-Token Ratio metric for text explanations. The approach incorporated data cleaning, preprocessing, and feature engineering with Pandas and NLTK, culminating in the preparation of training and testing splits for future modeling. The work established a robust foundation for iterative model development and data-driven insights, demonstrating depth in both exploratory analysis and pipeline structuring within a single feature cycle.
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
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