CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Array OperationsClusteringData AnalysisData CleaningData ManipulationData Preprocessing
예은
PROFILE
예은
Over a three-month period, Becherished1604 developed a suite of educational machine learning notebooks for the CUAI-CAU/2025_Basic_Track_Assignment repository, focusing on end-to-end workflows for data analysis and model evaluation. Using Python, Jupyter Notebook, and scikit-learn, they implemented practical examples covering data preprocessing, classification, regression, dimensionality reduction, and ensemble methods. The notebooks included hands-on exercises with NumPy and Pandas for data manipulation, as well as hyperparameter tuning with Hyperopt and GridSearchCV. Their work emphasized reproducibility and clear instructional guidance, providing ready-to-run resources that support scalable learning, reproducible experiments, and foundational exploration of risk analytics and model selection.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
5
Lines of code
19,500
Activity Months3
Your Network
1020 people
Work History
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Delivered a comprehensive ML Educational Notebooks suite enabling hands-on learning of core ML concepts. The notebooks cover classification metrics (accuracy, precision, recall), PCA visualization (Iris), regression basics, hyperparameter tuning, feature importance, and ensemble methods (Voting Classifier, Random Forest, Gradient Boosting, XGBoost) with Hyperopt for tuning. Established an initial setup for credit card default analysis to support risk analytics exploration. This work provides an end-to-end ML workflow—from data preparation to model evaluation—within a reproducible, Git-tracked framework. Key outcomes include ready-to-run notebooks for teaching and experimentation, clear guidance on evaluation and model selection, reproducible experiments across model families, and a solid foundation for future risk modeling projects.
3 Commits • 1 Features
May 1, 2025May 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Delivered a comprehensive ML Educational Notebooks suite enabling hands-on learning of core ML concepts. The notebooks cover classification metrics (accuracy, precision, recall), PCA visualization (Iris), regression basics, hyperparameter tuning, feature importance, and ensemble methods (Voting Classifier, Random Forest, Gradient Boosting, XGBoost) with Hyperopt for tuning. Established an initial setup for credit card default analysis to support risk analytics exploration. This work provides an end-to-end ML workflow—from data preparation to model evaluation—within a reproducible, Git-tracked framework. Key outcomes include ready-to-run notebooks for teaching and experimentation, clear guidance on evaluation and model selection, reproducible experiments across model families, and a solid foundation for future risk modeling projects.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Focused on delivering a practical ML prototyping notebook and improving model evaluation workflows. Key deliverable: a Jupyter Notebook detailing Regularized Linear Models (Ridge, Lasso, ElasticNet) with proper data scaling and evaluation across different alpha values and solvers, plus Logistic Regression with GridSearchCV-based hyperparameter tuning. This work enhances reproducibility, enables faster prototyping, and supports informed model selection in classification tasks.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Focused on delivering a practical ML prototyping notebook and improving model evaluation workflows. Key deliverable: a Jupyter Notebook detailing Regularized Linear Models (Ridge, Lasso, ElasticNet) with proper data scaling and evaluation across different alpha values and solvers, plus Logistic Regression with GridSearchCV-based hyperparameter tuning. This work enhances reproducibility, enables faster prototyping, and supports informed model selection in classification tasks.
March 2025
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Focused delivery of end-to-end educational notebooks establishing practical data analysis and ML workflows, complemented by course materials assets. This work enables scalable, hands-on learning, faster curriculum deployment, and improved learner outcomes. No major bugs reported this month; ongoing improvements include documentation and repo hygiene.
8 Commits • 3 Features
Mar 1, 2025March 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Focused delivery of end-to-end educational notebooks establishing practical data analysis and ML workflows, complemented by course materials assets. This work enables scalable, hands-on learning, faster curriculum deployment, and improved learner outcomes. No major bugs reported this month; ongoing improvements include documentation and repo hygiene.
March 2025
Activity
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Quality Metrics
Correctness83.4%
Maintainability83.4%
Architecture83.4%
Performance81.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
Array OperationsClassificationClusteringData AnalysisData CleaningData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesDimensionality ReductionEnsemble LearningGradient DescentHyperoptHyperparameter Tuning
Repositories Contributed To
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