HaiAu2501/EL4TF
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data PreprocessingDeep LearningMachine LearningPandasPyTorchPython
doclautc-1808
PROFILE
Doclautc-1808
During August 2025, Dao Chi Hien developed scalable stock data classification experiments in the HaiAu2501/EL4TF repository, focusing on the VN30 index. He implemented a multilayer perceptron model for both multi-class and binary classification, using Python and PyTorch within Jupyter Notebooks. His approach included per-stock training pipelines, data loading, and preprocessing with StandardScaler, ensuring reproducibility through per-stock checkpoints. By aggregating evaluation metrics such as balanced accuracy across stock files, he enabled robust model assessment. This work demonstrated depth in machine learning pipeline design, time series analysis, and notebook-driven experimentation, supporting scalable, deployment-ready workflows for financial data modeling.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
822
Activity Months1
Your Network
8 people
Work History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for HaiAu2501/EL4TF: Delivered scalable VN30 stock data classification experiments with per-stock training and notebook-based workflows. Implemented an MLP-based multi-class classification model and established a binary-class workflow with data loading, StandardScaler preprocessing, and aggregated evaluation using balanced accuracy across available stock files. Created per-stock checkpoints and experiment scaffolding to support reproducible, deployment-ready experimentation. This work demonstrates strong technical capabilities in Python-based ML pipelines, data preprocessing, model evaluation, and notebook-driven experimentation, while delivering business value through per-stock signals and scalable workflows.
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for HaiAu2501/EL4TF: Delivered scalable VN30 stock data classification experiments with per-stock training and notebook-based workflows. Implemented an MLP-based multi-class classification model and established a binary-class workflow with data loading, StandardScaler preprocessing, and aggregated evaluation using balanced accuracy across available stock files. Created per-stock checkpoints and experiment scaffolding to support reproducible, deployment-ready experimentation. This work demonstrates strong technical capabilities in Python-based ML pipelines, data preprocessing, model evaluation, and notebook-driven experimentation, while delivering business value through per-stock signals and scalable workflows.
August 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data PreprocessingDeep LearningMachine LearningPandasPyTorchPythonScikit-learnTime Series Analysis
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