CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPythonHTMLJSONJavaScriptSQL
Technical Skills
ClusteringData AnalysisData ClusteringData ManipulationData PreprocessingData Science
jungmin-park99
PROFILE
Jungmin-park99
Contributed to the CUAI-CAU/2025_Basic_Track_Assignment repository by developing a suite of educational data science tutorials and enhancing predictive modeling workflows. Built Jupyter Notebook-based resources covering foundational topics such as NumPy, Pandas, and Scikit-learn, along with practical clustering and regression examples to support onboarding and skill development. Implemented ensemble learning pipelines using Python, including RandomForest, XGBoost, and LightGBM, with hyperparameter tuning and feature importance visualization for real-world datasets. Prototyped PCA-based dimensionality reduction and maintained code quality through systematic cleanup of obsolete assets, improving reproducibility, reducing maintenance overhead, and enabling more effective team collaboration and stakeholder communication.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
19Total
Bugs
1
Commits
19
Features
4
Lines of code
32,801
Activity Months2
Your Network
1157 peopleWork History
May 2025
8 Commits • 3 Features
May 1, 2025May 2025 highlights for CUAI-CAU/2025_Basic_Track_Assignment. Delivered end-to-end predictive modeling enhancements using ensemble methods (Voting, RandomForest) and gradient boosting (XGBoost, LightGBM) with hyperparameter tuning and feature importance visualization across breast cancer and company-success datasets. Prototyped PCA-based dimensionality reduction with Iris and initial credit-card data experiments. Performed housekeeping to clean obsolete notebooks and PPTX, and added CUAI_BASIC_6 presentation to standardize stakeholder-ready materials. These efforts improved predictive analysis capabilities, reduced maintenance overhead, and strengthened the team's ability to explore, communicate, and derive business insights from model results.
8 Commits • 3 Features
May 1, 2025May 2025 highlights for CUAI-CAU/2025_Basic_Track_Assignment. Delivered end-to-end predictive modeling enhancements using ensemble methods (Voting, RandomForest) and gradient boosting (XGBoost, LightGBM) with hyperparameter tuning and feature importance visualization across breast cancer and company-success datasets. Prototyped PCA-based dimensionality reduction with Iris and initial credit-card data experiments. Performed housekeeping to clean obsolete notebooks and PPTX, and added CUAI_BASIC_6 presentation to standardize stakeholder-ready materials. These efforts improved predictive analysis capabilities, reduced maintenance overhead, and strengthened the team's ability to explore, communicate, and derive business insights from model results.
May 2025
March 2025
11 Commits • 1 Features
Mar 1, 2025March 2025 performance highlights for CUAI-CAU/2025_Basic_Track_Assignment: Delivered a cohesive Educational Data Science Tutorials suite (NumPy basics, Pandas data handling, Scikit-Learn fundamentals, clustering with K-Means, and regression workflows) as a group of notebooks and resources to accelerate foundational data science learning and ML workflow literacy. Conducted a targeted cleanup of obsolete notebooks to improve content quality and reduce maintenance overhead by removing outdated materials, including Basic_박정민_3주차_2.ipynb and Basic_박정민_3주차_1.ipynb. The changes improved onboarding clarity, reduced confusion, and lowered the risk of stale content. This work demonstrates strong iteration discipline and delivers measurable business value by enabling faster skill development and more reliable learning paths.
March 2025
11 Commits • 1 Features
Mar 1, 2025March 2025 performance highlights for CUAI-CAU/2025_Basic_Track_Assignment: Delivered a cohesive Educational Data Science Tutorials suite (NumPy basics, Pandas data handling, Scikit-Learn fundamentals, clustering with K-Means, and regression workflows) as a group of notebooks and resources to accelerate foundational data science learning and ML workflow literacy. Conducted a targeted cleanup of obsolete notebooks to improve content quality and reduce maintenance overhead by removing outdated materials, including Basic_박정민_3주차_2.ipynb and Basic_박정민_3주차_1.ipynb. The changes improved onboarding clarity, reduced confusion, and lowered the risk of stale content. This work demonstrates strong iteration discipline and delivers measurable business value by enabling faster skill development and more reliable learning paths.
Activity
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Quality Metrics
Correctness86.4%
Maintainability86.4%
Architecture86.4%
Performance85.4%
AI Usage25.2%
Skills & Technologies
Programming Languages
HTMLJSONJavaScriptJupyter NotebookPythonSQL
Technical Skills
ClusteringCode CleanupData AnalysisData ClusteringData ManipulationData PreprocessingData ScienceData VisualizationDimensionality ReductionEnsemble LearningFeature EngineeringFile ManagementGradient BoostingGradient DescentHyperOpt
Repositories Contributed To
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