HWTeng-Teaching/202509-ML-FinTech
Sep 2025 – Dec 2025
4 Months active
Languages Used
MarkdownPythonJupyter NotebookJSONNonePowerPoint
Technical Skills
Data AnalysisData VisualizationDocumentationJupyter NotebookPandasClustering
曾柏齡
PROFILE
曾柏齡
Bernie contributed to the HWTeng-Teaching/202509-ML-FinTech repository by developing a suite of machine learning and financial modeling tools over four months. He built Jupyter notebooks for clustering, regression, and trading strategy evaluation, integrating Python, Pandas, and scikit-learn to enable reproducible data analysis and visualization. Bernie focused on maintainability by refactoring code, consolidating assets, and removing deprecated directories, which improved onboarding and reduced technical debt. His work included implementing end-to-end workflows for data preprocessing, model training, and backtesting, as well as enhancing project documentation. The depth of his contributions established a robust foundation for ongoing analytics and collaboration.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
26Total
Bugs
2
Commits
26
Features
8
Lines of code
134,035
Activity Months4
Your Network
19 people
Work History
December 2025
9 Commits • 3 Features
Dec 1, 2025December 2025 monthly wrap-up for HWTeng-Teaching/202509-ML-FinTech: Key features delivered include a project cleanup and refactor to consolidate assets and remove deprecated directories, introduction of a benchmarking notebook for ML-driven trading model evaluation (including data preprocessing, model training, and backtesting), and the ML trading strategies presentations package with updates to streamline knowledge sharing. No critical bugs were reported; the primary focus was stabilizing the codebase and removing technical debt. Overall impact: improved maintainability, faster onboarding for ML experiments, and clearer dissemination of ML strategies. Technologies and skills demonstrated: Python-based data processing, ML model development and backtesting workflows, Jupyter notebooks, version control hygiene, and asset management for knowledge sharing.
9 Commits • 3 Features
Dec 1, 2025December 2025 monthly wrap-up for HWTeng-Teaching/202509-ML-FinTech: Key features delivered include a project cleanup and refactor to consolidate assets and remove deprecated directories, introduction of a benchmarking notebook for ML-driven trading model evaluation (including data preprocessing, model training, and backtesting), and the ML trading strategies presentations package with updates to streamline knowledge sharing. No critical bugs were reported; the primary focus was stabilizing the codebase and removing technical debt. Overall impact: improved maintainability, faster onboarding for ML experiments, and clearer dissemination of ML strategies. Technologies and skills demonstrated: Python-based data processing, ML model development and backtesting workflows, Jupyter notebooks, version control hygiene, and asset management for knowledge sharing.
December 2025
November 2025
7 Commits • 1 Features
Nov 1, 2025Month 2025-11 | Repository: HWTeng-Teaching/202509-ML-FinTech. Delivered an end-to-end financial data modeling toolkit in Jupyter notebooks, integrating ML models (logistic regression, LDA, KNN, polynomial regression, bootstrap, OLS, Ridge, Lasso, PCR) with BTC benchmarking, and added standardized evaluation metrics and visualizations across notebooks. Cleaned the repository by removing legacy HW5 and HW6 directories to reduce confusion and technical debt, improving maintainability and onboarding. Overall, these efforts deliver reproducible analytics, enable faster experimentation, and strengthen the foundation for future financial modeling features.
November 2025
7 Commits • 1 Features
Nov 1, 2025Month 2025-11 | Repository: HWTeng-Teaching/202509-ML-FinTech. Delivered an end-to-end financial data modeling toolkit in Jupyter notebooks, integrating ML models (logistic regression, LDA, KNN, polynomial regression, bootstrap, OLS, Ridge, Lasso, PCR) with BTC benchmarking, and added standardized evaluation metrics and visualizations across notebooks. Cleaned the repository by removing legacy HW5 and HW6 directories to reduce confusion and technical debt, improving maintainability and onboarding. Overall, these efforts deliver reproducible analytics, enable faster experimentation, and strengthen the foundation for future financial modeling features.
October 2025
6 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for HWTeng-Teaching/202509-ML-FinTech: Focused on delivering core clustering educational content and improving repository hygiene to support sustainment and onboarding.
6 Commits • 2 Features
Oct 1, 2025October 2025 monthly summary for HWTeng-Teaching/202509-ML-FinTech: Focused on delivering core clustering educational content and improving repository hygiene to support sustainment and onboarding.
October 2025
September 2025
4 Commits • 2 Features
Sep 1, 2025September 2025 Monthly Summary – HWTeng-Teaching/202509-ML-FinTech Key highlights include documentation-driven onboarding improvements and data exploration enablement for the College.csv dataset. No major bug fixes reported this month; focus was on foundational work to improve maintainability and reproducibility.
September 2025
4 Commits • 2 Features
Sep 1, 2025September 2025 Monthly Summary – HWTeng-Teaching/202509-ML-FinTech Key highlights include documentation-driven onboarding improvements and data exploration enablement for the College.csv dataset. No major bug fixes reported this month; focus was on foundational work to improve maintainability and reproducibility.
Activity
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Quality Metrics
Correctness93.4%
Maintainability90.0%
Architecture90.8%
Performance90.0%
AI Usage21.6%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownNonePowerPointPython
Technical Skills
API integrationClusteringData AnalysisData CleaningData ClusteringData ScienceData VisualizationDocumentationFile ManagementHierarchical ClusteringJupyter NotebookK-Means ClusteringMachine LearningMatplotlibNumPy
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