CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
PythonSQLJupyter Notebook
Technical Skills
Cross-validationData AnalysisData VisualizationFile ManagementGradient DescentGridSearchCV
HTET ARKAR
PROFILE
Htet Arkar
Over three months, Harkar developed a suite of educational data science notebooks and asset management improvements in the CUAI-CAU/2025_Basic_Track_Assignment repository. He created Jupyter notebooks demonstrating core machine learning workflows, including data preprocessing, regularized linear models, dimensionality reduction, and ensemble learning, using Python, scikit-learn, and pandas. His work emphasized reproducibility and clarity, with cross-validated hyperparameter tuning, visualizations, and comparative analyses to guide model selection. Harkar also reorganized project assets to streamline onboarding and prevent broken links. The depth of his contributions provided a robust foundation for experimentation and knowledge sharing, supporting both instructional and practical data science needs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
12Total
Bugs
0
Commits
12
Features
5
Lines of code
11,175
Activity Months3
Your Network
19 people
Work History
May 2025
2 Commits • 2 Features
May 1, 2025May 2025: Delivered two feature enhancements in CUAI-CAU/2025_Basic_Track_Assignment, focusing on dimensionality reduction visualization/comparison and ensemble model benchmarking with hyperparameter tuning. This work strengthens data exploration, model selection, and reproducibility, enabling faster data-driven decisions.
2 Commits • 2 Features
May 1, 2025May 2025: Delivered two feature enhancements in CUAI-CAU/2025_Basic_Track_Assignment, focusing on dimensionality reduction visualization/comparison and ensemble model benchmarking with hyperparameter tuning. This work strengthens data exploration, model selection, and reproducibility, enabling faster data-driven decisions.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 focused on enabling robust, reproducible experimentation with regularized linear models. Delivered a practical notebook introducing and evaluating Ridge, Lasso, and ElasticNet on linear models, including data loading, model training, cross-validated hyperparameter tuning, and visualization of coefficient behavior across alpha values. Also explored data scaling and polynomial feature transformations to assess their impact on model performance. This work is anchored in CUAI-CAU/2025_Basic_Track_Assignment, providing a clear path for scientists to compare regularization schemes and feature engineering strategies.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 focused on enabling robust, reproducible experimentation with regularized linear models. Delivered a practical notebook introducing and evaluating Ridge, Lasso, and ElasticNet on linear models, including data loading, model training, cross-validated hyperparameter tuning, and visualization of coefficient behavior across alpha values. Also explored data scaling and polynomial feature transformations to assess their impact on model performance. This work is anchored in CUAI-CAU/2025_Basic_Track_Assignment, providing a clear path for scientists to compare regularization schemes and feature engineering strategies.
March 2025
9 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03: Delivered foundational educational content and improved asset management for CUAI-CAU/2025_Basic_Track_Assignment. Two feature areas were addressed: (1) Educational Notebooks covering basic NumPy operations, pandas data analysis, machine learning tasks (classification and clustering with scikit-learn), and gradient-based optimization (gradient descent/SGD), including an attempted Boston housing dataset example; (2) Asset library organization and image improvements for Kaggle tutorials, including asset renames and directory restructuring to enhance accessibility.
9 Commits • 2 Features
Mar 1, 2025Monthly summary for 2025-03: Delivered foundational educational content and improved asset management for CUAI-CAU/2025_Basic_Track_Assignment. Two feature areas were addressed: (1) Educational Notebooks covering basic NumPy operations, pandas data analysis, machine learning tasks (classification and clustering with scikit-learn), and gradient-based optimization (gradient descent/SGD), including an attempted Boston housing dataset example; (2) Asset library organization and image improvements for Kaggle tutorials, including asset renames and directory restructuring to enhance accessibility.
March 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance86.6%
AI Usage23.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
Cross-validationData AnalysisData PreprocessingData VisualizationDimensionality ReductionEnsemble LearningFile ManagementGradient BoostingGradient DescentGridSearchCVHyperparameter TuningJupyter NotebookJupyter NotebooksK-Means ClusteringK-Nearest Neighbors
Repositories Contributed To
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