CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
ClusteringData AnalysisData ManipulationData ScienceData VisualizationDecision Trees
evaseo0078
PROFILE
Evaseo0078
Eva Seo developed a suite of reproducible data science and machine learning notebooks for the CUAI-CAU/2025_Basic_Track_Assignment repository over three months. She created end-to-end tutorials and benchmarking tools using Python and Jupyter Notebook, covering topics such as NumPy and Pandas basics, decision trees, K-Means clustering, regression analysis, and principal component analysis. Her work emphasized clean data preprocessing, model training, hyperparameter tuning, and visualization, enabling rapid onboarding and self-service experimentation. By structuring notebooks for clarity and reproducibility, Eva provided practical learning assets and established robust baselines for model comparison, supporting both educational and collaborative analytics within the team.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
13,238
Activity Months3
Your Network
19 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for CUAI-CAU project: delivered a self-contained PCA Notebook illustrating dimensionality reduction on Iris data, with end-to-end data scaling, PCA transformation, visualization, and a model-accuracy comparison; laid groundwork for credit-card dataset PCA explorations; progress aligns with team goals for reproducible analytics and educational material.
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for CUAI-CAU project: delivered a self-contained PCA Notebook illustrating dimensionality reduction on Iris data, with end-to-end data scaling, PCA transformation, visualization, and a model-accuracy comparison; laid groundwork for credit-card dataset PCA explorations; progress aligns with team goals for reproducible analytics and educational material.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment. Delivered an end-to-end Regression Model Benchmark Notebook comparing Ridge, Lasso, and ElasticNet. The notebook covers data loading, preprocessing, model training, hyperparameter tuning, and performance evaluation using RMSE and accuracy metrics, and explores scaling methods and polynomial features. This work establishes a reproducible baseline for regression models to inform feature engineering and model selection.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment. Delivered an end-to-end Regression Model Benchmark Notebook comparing Ridge, Lasso, and ElasticNet. The notebook covers data loading, preprocessing, model training, hyperparameter tuning, and performance evaluation using RMSE and accuracy metrics, and explores scaling methods and polynomial features. This work establishes a reproducible baseline for regression models to inform feature engineering and model selection.
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 performance: Delivered two feature bundles in CUAI-CAU/2025_Basic_Track_Assignment, enabling rapid onboarding and practical data science experimentation. Implemented NumPy basics and Pandas data handling tutorials, and a Machine Learning Notebooks Bundle covering Decision Tree, K-Means, and regression workflows. All work is version-controlled, organized for reproducibility, and positioned to drive self-service learning. No major bugs fixed this month; stability improvements were achieved through structured notebooks and clean commits. This work enhances data literacy, accelerates experimentation, and creates reusable learning assets across the team.
4 Commits • 2 Features
Mar 1, 2025March 2025 performance: Delivered two feature bundles in CUAI-CAU/2025_Basic_Track_Assignment, enabling rapid onboarding and practical data science experimentation. Implemented NumPy basics and Pandas data handling tutorials, and a Machine Learning Notebooks Bundle covering Decision Tree, K-Means, and regression workflows. All work is version-controlled, organized for reproducibility, and positioned to drive self-service learning. No major bugs fixed this month; stability improvements were achieved through structured notebooks and clean commits. This work enhances data literacy, accelerates experimentation, and creates reusable learning assets across the team.
March 2025
Activity
Loading activity data...
Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance83.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ClusteringCross-validationData AnalysisData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesDimensionality ReductionElasticNet RegressionFeature ScalingGradient DescentGrid SearchHyperparameter TuningJupyter Notebook
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline