HaiAu2501/EL4TF
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
ClassificationData PreprocessingData ScienceMachine LearningMulti-label ClassificationNumPy
Tamnt240904
PROFILE
Tamnt240904
During August 2025, Tam Nguyen developed an SVM modeling and experimentation framework for the HaiAu2501/EL4TF repository, targeting the VN30 stock dataset and synthetic data. He implemented data preprocessing utilities in Python and Pandas to standardize features, and designed workflows for binary, multi-class, and multi-label classification using Scikit-learn. Tam integrated RandomizedSearchCV with TimeSeriesSplit for robust model selection, and automated the generation of model checkpoints and detailed result summaries, including per-symbol accuracy and confusion matrices. His work enabled reproducible, time-series-aware experiments, strengthening the predictive analytics pipeline and supporting actionable insights for portfolio decision-making in financial data science.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
1
Lines of code
3,221
Activity Months1
Your Network
13 peopleSame Organization
@sis.hust.edu.vn5
Work History
August 2025
3 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for HaiAu2501/EL4TF: Delivered a comprehensive SVM modeling and experimentation framework across binary, multi-class, and multi-label tasks for the VN30 stock dataset and synthetic data. Implemented data preprocessing utilities, model training/selection (including RandomizedSearchCV with TimeSeriesSplit), and automated generation of model checkpoints and result summaries (accuracy per symbol, confusion matrices). This work strengthens the predictive analytics pipeline, enabling reproducible experiments and actionable insights for portfolio decision-making.
3 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for HaiAu2501/EL4TF: Delivered a comprehensive SVM modeling and experimentation framework across binary, multi-class, and multi-label tasks for the VN30 stock dataset and synthetic data. Implemented data preprocessing utilities, model training/selection (including RandomizedSearchCV with TimeSeriesSplit), and automated generation of model checkpoints and result summaries (accuracy per symbol, confusion matrices). This work strengthens the predictive analytics pipeline, enabling reproducible experiments and actionable insights for portfolio decision-making.
August 2025
Activity
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Quality Metrics
Correctness83.4%
Maintainability80.0%
Architecture80.0%
Performance63.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ClassificationData PreprocessingData ScienceMachine LearningMulti-label ClassificationNumPyPandasPythonScikit-learnSupport Vector Machines (SVM)Time Series Analysis
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