CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
JSONJupyter NotebookPythonSQL
Technical Skills
Array OperationsCross-ValidationData AnalysisData ManipulationData PreprocessingData Science
배동혁
PROFILE
배동혁
Over a three-month period, contributed a suite of nine machine learning education assets to the CUAI-CAU/2025_Basic_Track_Assignment repository, focusing on reproducible workflows and onboarding support. Developed Jupyter Notebooks covering foundational topics such as NumPy operations, regression and classification models, ensemble methods, and dimensionality reduction, using Python and scikit-learn. Implemented practical examples on datasets like Titanic, MNIST, Iris, and Boston housing, emphasizing model evaluation, hyperparameter tuning, and visualization with Matplotlib and Seaborn. Prioritized repository organization by standardizing file naming and removing outdated artifacts, resulting in a maintainable resource for learners and streamlined knowledge transfer without major bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
9
Lines of code
25,301
Activity Months3
Your Network
1157 peopleWork History
May 2025
5 Commits • 5 Features
May 1, 2025May 2025: Delivered a cohesive set of five feature notebooks in CUAI-CAU/2025_Basic_Track_Assignment, focusing on practical ML model evaluation, tree-based methods, regression trees, dimensionality reduction, and advanced ensemble/hyperparameter tuning. The assets are designed for reproducibility and business-ready demonstrations, covering datasets such as Titanic, MNIST, Iris, Boston housing, and breast cancer.
5 Commits • 5 Features
May 1, 2025May 2025: Delivered a cohesive set of five feature notebooks in CUAI-CAU/2025_Basic_Track_Assignment, focusing on practical ML model evaluation, tree-based methods, regression trees, dimensionality reduction, and advanced ensemble/hyperparameter tuning. The assets are designed for reproducibility and business-ready demonstrations, covering datasets such as Titanic, MNIST, Iris, Boston housing, and breast cancer.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 performance summary: Key deliverable was an ML Tutorials Notebook for regression and classification in CUAI-CAU/2025_Basic_Track_Assignment, providing end-to-end workflows for linear regression models (Ridge, Lasso, ElasticNet) and Logistic Regression, including data transformation and hyperparameter tuning. The notebook serves as a practical guide for applying ML algorithms to real datasets and improving reproducibility of learning resources.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 performance summary: Key deliverable was an ML Tutorials Notebook for regression and classification in CUAI-CAU/2025_Basic_Track_Assignment, providing end-to-end workflows for linear regression models (Ridge, Lasso, ElasticNet) and Logistic Regression, including data transformation and hyperparameter tuning. The notebook serves as a practical guide for applying ML algorithms to real datasets and improving reproducibility of learning resources.
March 2025
11 Commits • 3 Features
Mar 1, 2025March 2025 (CUAI-CAU/2025_Basic_Track_Assignment) — Delivered foundational ML education assets and improved repository hygiene to support onboarding, learning outcomes, and reproducibility. Key deliverables include Kaggle Basics Data/Assets for the 1-week track, a set of foundational ML notebooks (NumPy basics, Titanic with Pandas, regression, ensemble, and clustering), and standardized naming with cleanup of outdated notebooks. No major bugs fixed this month; the work focused on feature delivery and maintainability. Total commits: 11 across three feature areas, demonstrating consistent Git discipline.
11 Commits • 3 Features
Mar 1, 2025March 2025 (CUAI-CAU/2025_Basic_Track_Assignment) — Delivered foundational ML education assets and improved repository hygiene to support onboarding, learning outcomes, and reproducibility. Key deliverables include Kaggle Basics Data/Assets for the 1-week track, a set of foundational ML notebooks (NumPy basics, Titanic with Pandas, regression, ensemble, and clustering), and standardized naming with cleanup of outdated notebooks. No major bugs fixed this month; the work focused on feature delivery and maintainability. Total commits: 11 across three feature areas, demonstrating consistent Git discipline.
March 2025
Activity
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Quality Metrics
Correctness89.4%
Maintainability88.2%
Architecture88.2%
Performance87.2%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSONJupyter NotebookPythonSQL
Technical Skills
Array OperationsClassificationClassification MetricsCross-ValidationData AnalysisData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesDimensionality ReductionEducational Content CreationEnsemble MethodsFeature ScalingFile Management
Repositories Contributed To
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