HaiAu2501/EL4TF
Aug 2025 – Sep 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Binary ClassificationData GenerationData NormalizationData PreprocessingData ScienceFeature Engineering
Lam
PROFILE
Lam
Developed a reproducible binary classification experimentation workflow in the HaiAu2501/EL4TF repository, focusing on rapid prototyping and robust model evaluation using Python and Jupyter Notebook. Built and iteratively refined a Logistic Regression pipeline on synthetic data, incorporating data generation, normalization, feature selection, and multi-seed analysis to assess model stability and feature engineering impact. Enhanced interpretability by evaluating time-cycle features and introducing Random Fourier Features for deeper experimentation. Emphasized reproducibility and traceability through detailed documentation, explicit execution tracking, and consolidated performance reporting, enabling data-driven decision making and reliable experimentation practices without critical bugs reported during the development period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
3
Lines of code
1,524
Activity Months2
Your Network
8 peopleShared Repositories
8
Your NameMember
bin1st090104Member
doclautc-1808Member
pqnhoangMember
HaiAu2501Member
khangMember
KouhakouuMember
Tamnt240904Member
Work History
September 2025
3 Commits • 2 Features
Sep 1, 20252025-09 Monthly Summary: Delivered deeper evaluation capabilities for time-cycle features in a binary classification model and launched an experimental Random Fourier Features (RFF) pipeline with Logistic Regression. These efforts improved feature interpretability, evaluation rigor, and informed model direction for production use. Reproducibility and documentation were enhanced through notebook-based analyses and multi-seed experimentation.
3 Commits • 2 Features
Sep 1, 20252025-09 Monthly Summary: Delivered deeper evaluation capabilities for time-cycle features in a binary classification model and launched an experimental Random Fourier Features (RFF) pipeline with Logistic Regression. These efforts improved feature interpretability, evaluation rigor, and informed model direction for production use. Reproducibility and documentation were enhanced through notebook-based analyses and multi-seed experimentation.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for HaiAu2501/EL4TF focused on delivering a reproducible binary classification experimentation workflow using Logistic Regression on synthetic data. The work emphasizes business value through rapid prototyping, clear performance metrics, and stable experimentation practices.
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for HaiAu2501/EL4TF focused on delivering a reproducible binary classification experimentation workflow using Logistic Regression on synthetic data. The work emphasizes business value through rapid prototyping, clear performance metrics, and stable experimentation practices.
Activity
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Quality Metrics
Correctness82.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage24.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Binary ClassificationData AnalysisData GenerationData NormalizationData PreprocessingData ScienceFeature EngineeringFeature SelectionJupyter NotebookLinear RegressionLogistic RegressionMachine LearningModel EvaluationPythonRandom Fourier Features
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