CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Array OperationsCross-ValidationCross-validationData AnalysisData ClusteringData Encoding
lgy7721
PROFILE
Lgy7721
Over a two-month period, Lee Gyuyoung developed a suite of educational machine learning notebooks for the CUAI-CAU/2025_Basic_Track_Assignment repository. Lee focused on building end-to-end workflows in Python and Jupyter Notebook, covering data loading, preprocessing, model training, evaluation, and visualization. The notebooks addressed core topics such as regression, clustering, and classification using libraries like NumPy, Pandas, and scikit-learn. By establishing reproducible, version-controlled content, Lee enabled scalable onboarding and hands-on learning for new data science students. The work demonstrated depth in both technical implementation and curriculum design, providing reusable artifacts that support assessment, reproducibility, and curriculum expansion without reported bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
3
Lines of code
14,714
Activity Months2
Your Network
24 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Implemented ML assignment notebooks in CUAI-CAU/2025_Basic_Track_Assignment to deliver practical ML learning artifacts. Focused on end-to-end coverage of data loading, model training, evaluation, and visualization across three notebooks for accuracy metrics, decision tree visualization and overfitting analysis, and regression trees. This work enhances learner onboarding, assessment readiness, and reproducibility.
1 Commits • 1 Features
May 1, 2025May 2025: Implemented ML assignment notebooks in CUAI-CAU/2025_Basic_Track_Assignment to deliver practical ML learning artifacts. Focused on end-to-end coverage of data loading, model training, evaluation, and visualization across three notebooks for accuracy metrics, decision tree visualization and overfitting analysis, and regression trees. This work enhances learner onboarding, assessment readiness, and reproducibility.
May 2025
March 2025
15 Commits • 2 Features
Mar 1, 2025March 2025 – CUAI-CAU/2025_Basic_Track_Assignment: Delivered two feature-rich notebook suites establishing foundational data science skills and end-to-end ML workflows. Features delivered: NumPy and Pandas Fundamentals Notebooks with data loading and basic manipulation; Machine Learning Notebooks and Tutorials covering data loading, preprocessing, model training/evaluation, clustering, regression, and classification across Iris, Titanic, and Kaggle tasks. Major bugs fixed: No explicit major bugs reported; ongoing refinements and asset consistency updates were performed. Overall impact: Ready-to-use educational content enabling faster onboarding, reusable curriculum for scalable learning, and demonstrable value to learners and instructors. Technologies/skills demonstrated: Python, NumPy, Pandas, scikit-learn-like ML workflows, notebook-based pedagogy, data loading pipelines, visualization assets.
March 2025
15 Commits • 2 Features
Mar 1, 2025March 2025 – CUAI-CAU/2025_Basic_Track_Assignment: Delivered two feature-rich notebook suites establishing foundational data science skills and end-to-end ML workflows. Features delivered: NumPy and Pandas Fundamentals Notebooks with data loading and basic manipulation; Machine Learning Notebooks and Tutorials covering data loading, preprocessing, model training/evaluation, clustering, regression, and classification across Iris, Titanic, and Kaggle tasks. Major bugs fixed: No explicit major bugs reported; ongoing refinements and asset consistency updates were performed. Overall impact: Ready-to-use educational content enabling faster onboarding, reusable curriculum for scalable learning, and demonstrable value to learners and instructors. Technologies/skills demonstrated: Python, NumPy, Pandas, scikit-learn-like ML workflows, notebook-based pedagogy, data loading pipelines, visualization assets.
Activity
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Quality Metrics
Correctness81.4%
Maintainability78.8%
Architecture76.2%
Performance76.4%
AI Usage22.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Array OperationsCross-ValidationCross-validationData AnalysisData ClusteringData EncodingData ExplorationData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesElasticNet RegressionFeature EngineeringFeature Scaling
Repositories Contributed To
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