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
Over four months, contributed to the HWTeng-Teaching/202509-ML-FinTech repository by developing eight features and addressing two bugs, focusing on machine learning-driven financial modeling and educational tooling. Built end-to-end workflows in Python and Jupyter Notebook, integrating clustering algorithms, regression models, and benchmarking pipelines for financial data analysis. Enhanced project maintainability through systematic documentation, repository cleanup, and onboarding improvements, while consolidating assets and removing deprecated code. Leveraged skills in data analysis, data visualization, and scikit-learn to deliver reproducible analytics and streamlined experimentation. The work established a robust foundation for future collaboration, faster onboarding, and scalable machine learning experimentation within the project.
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 peopleWork 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
Repositories Contributed To
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