
Developed a suite of machine learning and data analysis assets in the HWTeng-Teaching/202409-ML-FinTech repository, focusing on financial modeling and educational resources. Delivered reproducible Jupyter Notebooks in Python for model comparison on market direction and auto MPG prediction, implementing classifiers such as Logistic Regression, LDA, QDA, KNN, and Naive Bayes using Scikit-learn. Enhanced experimentation by exploring hyperparameters and evaluating models with confusion matrices and accuracy metrics. Created investor-facing presentations and XGBoost analysis notebooks for stock market data, while maintaining clear documentation and version control. The work emphasized technical rigor, reproducibility, and accessible learning materials for data science applications.
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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