HWTeng-Teaching/202509-ML-FinTech
Sep 2025 – Dec 2025
4 Months active
Languages Used
Jupyter NotebookMarkdownPythonNonePDF
Technical Skills
Code Repository ManagementData AnalysisData ScienceData VisualizationDocumentationExploratory Data Analysis
ambervo3122
PROFILE
Ambervo3122
Quynhtram Vo developed a suite of machine learning and data analysis notebooks for the HWTeng-Teaching/202509-ML-FinTech repository, focusing on financial modeling, trading strategy evaluation, and educational workflows. Over four months, she implemented end-to-end pipelines for stock market analysis, regression modeling, and clustering, using Python, Jupyter Notebook, and libraries such as pandas and scikit-learn. Her work emphasized reproducibility and maintainability through standardized documentation, project structure cleanup, and artifact management. By integrating feature selection, model evaluation metrics, and benchmarking frameworks, she enabled rapid experimentation and reliable analytics, supporting both instructional needs and data-driven decision-making in financial technology contexts.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
44Total
Bugs
1
Commits
44
Features
12
Lines of code
28,777
Activity Months4
Your Network
19 people
Work History
December 2025
12 Commits • 2 Features
Dec 1, 2025Month: 2025-12 | Focus: Feature development and repository hygiene for ML FinTech project. Delivered an end-to-end trading strategy ML notebooks and evaluation framework with data loading, preprocessing, model training (OLS, LASSO, XGBoost, Random Forest), feature selection, and evaluation metrics (RMSE, R², MAE) for financial time series and stock price predictions, including benchmark comparisons and feature importance analyses. Completed documentation cleanup and archival of project artifacts to streamline the repository and enhance onboarding. Result: reusable pipeline and clearer project structure.
12 Commits • 2 Features
Dec 1, 2025Month: 2025-12 | Focus: Feature development and repository hygiene for ML FinTech project. Delivered an end-to-end trading strategy ML notebooks and evaluation framework with data loading, preprocessing, model training (OLS, LASSO, XGBoost, Random Forest), feature selection, and evaluation metrics (RMSE, R², MAE) for financial time series and stock price predictions, including benchmark comparisons and feature importance analyses. Completed documentation cleanup and archival of project artifacts to streamline the repository and enhance onboarding. Result: reusable pipeline and clearer project structure.
December 2025
November 2025
8 Commits • 3 Features
Nov 1, 2025Nov 2025 (HWTeng-Teaching/202509-ML-FinTech): Delivered three feature notebook suites for finance-focused ML education and analytics. Stock market analysis and forecasting notebooks enable EDA, strategy benchmarking, and volatility forecasting using regression and machine learning. Boston housing educational notebooks cover regression analysis, cross-validation, and significance testing. Statistical modeling notebooks extend analyses with stepwise selection, regression methods, and polynomial regression across multiple datasets. No major bugs reported; focus on stability and reproducibility. Business impact: accelerates data-driven decision making, enhances portfolio analytics readiness, and strengthens ML education with reproducible notebooks. Technologies demonstrated: Python, Jupyter, pandas, scikit-learn, statsmodels; modeling workflows, data visualization, and notebook automation.
November 2025
8 Commits • 3 Features
Nov 1, 2025Nov 2025 (HWTeng-Teaching/202509-ML-FinTech): Delivered three feature notebook suites for finance-focused ML education and analytics. Stock market analysis and forecasting notebooks enable EDA, strategy benchmarking, and volatility forecasting using regression and machine learning. Boston housing educational notebooks cover regression analysis, cross-validation, and significance testing. Statistical modeling notebooks extend analyses with stepwise selection, regression methods, and polynomial regression across multiple datasets. No major bugs reported; focus on stability and reproducibility. Business impact: accelerates data-driven decision making, enhances portfolio analytics readiness, and strengthens ML education with reproducible notebooks. Technologies demonstrated: Python, Jupyter, pandas, scikit-learn, statsmodels; modeling workflows, data visualization, and notebook automation.
October 2025
3 Commits • 2 Features
Oct 1, 2025Concise monthly summary for 2025-10 focused on delivering data-science notebooks and repository maintenance that enable rapid experimentation and reliable analytics workflows for HWTeng-Teaching/202509-ML-FinTech.
3 Commits • 2 Features
Oct 1, 2025Concise monthly summary for 2025-10 focused on delivering data-science notebooks and repository maintenance that enable rapid experimentation and reliable analytics workflows for HWTeng-Teaching/202509-ML-FinTech.
October 2025
September 2025
21 Commits • 5 Features
Sep 1, 2025In September 2025, HWTeng-Teaching/202509-ML-FinTech established a reproducible, well-documented foundation for teaching ML in FinTech. The work delivered enhances onboarding, maintainability, and course execution while demonstrating strong version-control and documentation practices.
September 2025
21 Commits • 5 Features
Sep 1, 2025In September 2025, HWTeng-Teaching/202509-ML-FinTech established a reproducible, well-documented foundation for teaching ML in FinTech. The work delivered enhances onboarding, maintainability, and course execution while demonstrating strong version-control and documentation practices.
Activity
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Quality Metrics
Correctness94.8%
Maintainability91.8%
Architecture91.8%
Performance91.4%
AI Usage24.2%
Skills & Technologies
Programming Languages
Jupyter NotebookMarkdownNonePDFPython
Technical Skills
ClusteringCode Repository ManagementData AnalysisData ClusteringData ScienceData VisualizationDocumentationExploratory Data AnalysisFile ManagementHierarchical ClusteringJupyterJupyter NotebookK-MeansK-Means AlgorithmK-Means Clustering
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