
Rebecca contributed to the HWTeng-Teaching/202409-ML-FinTech repository by developing machine learning notebooks and investor-facing materials focused on financial data analysis. She built reproducible Jupyter Notebooks in Python to compare classification models for market direction and auto MPG prediction, implementing end-to-end pipelines with Scikit-learn for model training, evaluation, and hyperparameter exploration. Rebecca also created educational resources on bias-variance tradeoff, regularization, and regression methods, enhancing the repository’s instructional value. Her work included XGBoost-based stock market analysis and portfolio growth presentations, demonstrating technical rigor in both data science workflows and content management, with careful version control and clear documentation throughout each deliverable.

Concise monthly summary for 2024-12 highlighting key deliverables, impact, and capabilities demonstrated across the HWTeng-Teaching/202409-ML-FinTech repository. Focused on delivering investor-facing assets and data-science materials with clear business value and technical rigor.
Concise monthly summary for 2024-12 highlighting key deliverables, impact, and capabilities demonstrated across the HWTeng-Teaching/202409-ML-FinTech repository. Focused on delivering investor-facing assets and data-science materials with clear business value and technical rigor.
Monthly summary for 2024-11: Delivered end-to-end ML/FinTech repo updates in HWTeng-Teaching/202409-ML-FinTech, focusing on model comparison for MPG prediction, educational ML notebooks and visualizations, and expanded documentation resources. No major bugs reported this month. The work enhanced experimentation velocity, learning materials, and access to reference resources for HW1111.
Monthly summary for 2024-11: Delivered end-to-end ML/FinTech repo updates in HWTeng-Teaching/202409-ML-FinTech, focusing on model comparison for MPG prediction, educational ML notebooks and visualizations, and expanded documentation resources. No major bugs reported this month. The work enhanced experimentation velocity, learning materials, and access to reference resources for HW1111.
In October 2024, delivered a reproducible ML Model Comparison Notebook for Market Direction in HWTeng-Teaching/202409-ML-FinTech. The notebook loads weekly financial data via ISLP, compares multiple classifiers (Logistic Regression, LDA, QDA, KNN, Naive Bayes), and evaluates performance with confusion matrices and accuracy metrics. It also explores KNN hyperparameters to identify an effective configuration, establishing a scalable framework for model selection and experimentation.
In October 2024, delivered a reproducible ML Model Comparison Notebook for Market Direction in HWTeng-Teaching/202409-ML-FinTech. The notebook loads weekly financial data via ISLP, compares multiple classifiers (Logistic Regression, LDA, QDA, KNN, Naive Bayes), and evaluates performance with confusion matrices and accuracy metrics. It also explores KNN hyperparameters to identify an effective configuration, establishing a scalable framework for model selection and experimentation.
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