
Over three months, Sunhong Hong developed a comprehensive suite of educational data science notebooks and resources for the CUAI-CAU/2025_Basic_Track_Assignment repository. He designed end-to-end Jupyter Notebooks covering topics from NumPy and pandas basics to machine learning model evaluation, including experiments with Ridge, Lasso, and ElasticNet regression, as well as classification and dimensionality reduction using PCA. His work emphasized reproducibility and maintainability, organizing assets and ensuring clear commit histories. By leveraging Python, scikit-learn, and visualization libraries like Matplotlib and Seaborn, Sunhong established reproducible baselines and workflows that support both learner onboarding and efficient model experimentation.

May 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Delivered two ML experimentation notebooks and one dimensionality reduction notebook, establishing a reproducible evaluation pipeline and visualization workflows that enable data-driven model selection and feature engineering.
May 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment: Delivered two ML experimentation notebooks and one dimensionality reduction notebook, establishing a reproducible evaluation pipeline and visualization workflows that enable data-driven model selection and feature engineering.
April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment. Delivered an end-to-end ML Model Experiment Notebook that provides data preprocessing and model evaluation for the Boston Housing dataset and a cancer dataset, including Ridge, Lasso, ElasticNet with various alpha values and scaling, plus Logistic Regression experiments with multiple solvers and hyperparameters. The work establishes reproducible baselines and accelerates future model iteration. Commits include 0956365e90671652235469f77ad0a71a9304befb.
April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment. Delivered an end-to-end ML Model Experiment Notebook that provides data preprocessing and model evaluation for the Boston Housing dataset and a cancer dataset, including Ridge, Lasso, ElasticNet with various alpha values and scaling, plus Logistic Regression experiments with multiple solvers and hyperparameters. The work establishes reproducible baselines and accelerates future model iteration. Commits include 0956365e90671652235469f77ad0a71a9304befb.
Monthly summary for 2025-03: Delivered a complete educational data science track within CUAI-CAU/2025_Basic_Track_Assignment, featuring end-to-end notebooks and organized assets. The initiative focused on learner onboarding, reproducible examples, and maintainable materials to accelerate self-guided learning and instructor support.
Monthly summary for 2025-03: Delivered a complete educational data science track within CUAI-CAU/2025_Basic_Track_Assignment, featuring end-to-end notebooks and organized assets. The initiative focused on learner onboarding, reproducible examples, and maintainable materials to accelerate self-guided learning and instructor support.
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