CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
ClusteringData AnalysisData PreprocessingData VisualizationDecision TreesGradient Descent
seungmiiii
PROFILE
Seungmiiii
Over a three-month period, rlatmdal1617@gmail.com developed a suite of educational data science and machine learning assets within the CUAI-CAU/2025_Basic_Track_Assignment repository. They created reproducible Jupyter Notebooks and Python scripts covering data preprocessing, model evaluation, and ensemble methods, supporting both onboarding and curriculum delivery. Their work included scalable frameworks for hyperparameter tuning and model selection, integrating tools such as Scikit-learn, Optuna, and LightGBM. By delivering organized experimentation pipelines and presentation assets, they enabled repeatable experiments and streamlined stakeholder communication. The depth of their contributions is reflected in robust, well-documented workflows that facilitate both learning and rapid model iteration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
11Total
Bugs
0
Commits
11
Features
4
Lines of code
12,140
Activity Months3
Your Network
19 people
Work History
May 2025
4 Commits • 1 Features
May 1, 2025May 2025: Delivered a focused machine learning model development, evaluation, and tuning suite within CUAI-CAU/2025_Basic_Track_Assignment. Implemented end-to-end experimentation notebooks for classification tasks, integrating diverse models (Voting Classifier, Random Forest, Gradient Boosting, XGBoost, LightGBM) with evaluation insights. Established hyperparameter tuning workflows using GridSearch, Hyperopt, and Optuna, and created an ensemble training notebook plus a binary presentation asset in the Basic_Project to accompany ML experiments. This work improves reproducibility, accelerates model iteration, and provides ready-to-share artifacts for stakeholder discussions.
4 Commits • 1 Features
May 1, 2025May 2025: Delivered a focused machine learning model development, evaluation, and tuning suite within CUAI-CAU/2025_Basic_Track_Assignment. Implemented end-to-end experimentation notebooks for classification tasks, integrating diverse models (Voting Classifier, Random Forest, Gradient Boosting, XGBoost, LightGBM) with evaluation insights. Established hyperparameter tuning workflows using GridSearch, Hyperopt, and Optuna, and created an ensemble training notebook plus a binary presentation asset in the Basic_Project to accompany ML experiments. This work improves reproducibility, accelerates model iteration, and provides ready-to-share artifacts for stakeholder discussions.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focused on delivering a scalable ML model evaluation and hyperparameter tuning framework within the CUAI-CAU/2025_Basic_Track_Assignment repository. The work established a reusable pipeline for evaluating Ridge, Lasso, ElasticNet, and Logistic Regression models with proper data scaling, cross-validation, and automated hyperparameter tuning, enabling robust model selection and repeatable experiments.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focused on delivering a scalable ML model evaluation and hyperparameter tuning framework within the CUAI-CAU/2025_Basic_Track_Assignment repository. The work established a reusable pipeline for evaluating Ridge, Lasso, ElasticNet, and Logistic Regression models with proper data scaling, cross-validation, and automated hyperparameter tuning, enabling robust model selection and repeatable experiments.
March 2025
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary: Delivered core educational materials for CUAI-CAU/2025_Basic_Track_Assignment to accelerate onboarding and curriculum delivery. Key features: Educational Data Science Tutorials and Track Assignment Image Assets. No major bugs reported. Impact: improved learner onboarding, reproducible notebooks, and ready-to-use visuals for instructors. Technologies/skills demonstrated: Python data science stack (NumPy, Pandas), ML concepts (decision trees, encoding, clustering, regression), asset management, Git-based collaboration.
6 Commits • 2 Features
Mar 1, 2025March 2025 monthly summary: Delivered core educational materials for CUAI-CAU/2025_Basic_Track_Assignment to accelerate onboarding and curriculum delivery. Key features: Educational Data Science Tutorials and Track Assignment Image Assets. No major bugs reported. Impact: improved learner onboarding, reproducible notebooks, and ready-to-use visuals for instructors. Technologies/skills demonstrated: Python data science stack (NumPy, Pandas), ML concepts (decision trees, encoding, clustering, regression), asset management, Git-based collaboration.
March 2025
Activity
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Quality Metrics
Correctness91.0%
Maintainability89.2%
Architecture89.2%
Performance85.4%
AI Usage27.2%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ClassificationClassification MetricsClusteringData AnalysisData PreprocessingData ScienceData VisualizationDecision TreesEnsemble MethodsGradient DescentHyperoptHyperparameter TuningJupyter NotebookK-MeansLabel Encoding
Repositories Contributed To
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