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becca5596

PROFILE

Becca5596

Rebecca contributed to the HWTeng-Teaching/202409-ML-FinTech repository by developing machine learning notebooks and investor-facing presentation materials over a three-month period. She built reproducible Jupyter Notebooks in Python for model comparison on financial datasets, implementing classifiers such as Logistic Regression, LDA, QDA, KNN, and Naive Bayes, and evaluated them using confusion matrices and accuracy metrics. Rebecca also created educational resources on bias-variance tradeoff, regularization, and feature selection, and managed documentation assets. Her work included XGBoost-based stock analysis and portfolio growth presentations, demonstrating depth in data analysis, financial modeling, and version control, with a focus on clarity and technical rigor.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

14Total
Bugs
0
Commits
14
Features
6
Lines of code
3,233
Activity Months3

Work History

December 2024

7 Commits • 2 Features

Dec 1, 2024

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.

November 2024

6 Commits • 3 Features

Nov 1, 2024

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.

October 2024

1 Commits • 1 Features

Oct 1, 2024

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.

Activity

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Quality Metrics

Correctness92.8%
Maintainability91.4%
Architecture91.4%
Performance91.4%
AI Usage23.0%

Skills & Technologies

Programming Languages

Jupyter NotebookPython

Technical Skills

Bias-Variance TradeoffData AnalysisData VisualizationFeature SelectionFinancial ModelingISLPJupyter NotebookJupyter NotebooksKNNLDALasso RegressionLogistic RegressionMachine LearningMathematical FunctionsMatplotlib

Repositories Contributed To

1 repo

Overview of all repositories you've contributed to across your timeline

HWTeng-Teaching/202409-ML-FinTech

Oct 2024 Dec 2024
3 Months active

Languages Used

Jupyter NotebookPython

Technical Skills

Data AnalysisISLPKNNLDALogistic RegressionMachine Learning